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Cui Y, Fan R, Cheng Y, Sun A, Xu Z, Schwier M, Li L, Lin S, Schoebinger M, Xiao Y, Liu S. Image Quality Assessment of a Deep Learning-Based Automatic Bone Removal Algorithm for Cervical CTA. J Comput Assist Tomogr 2024:00004728-990000000-00339. [PMID: 39095057 DOI: 10.1097/rct.0000000000001637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
BACKGROUND The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA). MATERIALS AND METHODS A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed. RESULTS Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity (r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm. CONCLUSIONS The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.
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Affiliation(s)
- Yuanyuan Cui
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Rongrong Fan
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yuxin Cheng
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - An Sun
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | | | | | | | | | | | - Yi Xiao
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Shiyuan Liu
- From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
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Guo X, Liu T, Yang Y, Dai J, Wang L, Tang D, Sun H. Automatic Segmentation of Type A Aortic Dissection on Computed Tomography Images Using Deep Learning Approach. Diagnostics (Basel) 2024; 14:1332. [PMID: 39001223 PMCID: PMC11240582 DOI: 10.3390/diagnostics14131332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/16/2024] [Accepted: 06/19/2024] [Indexed: 07/16/2024] Open
Abstract
PURPOSE Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment. METHODS This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared. RESULTS Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89-6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35-9.48%). CONCLUSIONS The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.
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Affiliation(s)
- Xiaoya Guo
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Tianshu Liu
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yi Yang
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jianxin Dai
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Haoliang Sun
- Department of Cardiovascular Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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Jung JH, Oh HM, Jeong GJ, Kim TW, Koo HJ, Lee JG, Yang DH. ZOZI-Seg: A transformer and UNet cascade network with Zoom-Out and Zoom-In scheme for aortic dissection segmentation in enhanced CT images. Comput Biol Med 2024; 175:108494. [PMID: 38688124 DOI: 10.1016/j.compbiomed.2024.108494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 03/20/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND & OBJECTIVE Aortic dissection (AD) is a serious condition requiring rapid and accurate diagnosis. In this study, we aimed to improve the diagnostic accuracy of AD by presenting a novel method for aortic segmentation in computed tomography images that uses a combination of a transformer and a UNet cascade network with a Zoom-Out and Zoom-In scheme (ZOZI-seg). METHODS The proposed method segments each compartment of the aorta, comprising the true lumen (TL), false lumen (FL), and thrombosis (TH) using a cascade strategy that captures both the global context (anatomical structure) and the local detail texture based on the dynamic patch size with ZOZI schemes. The ZOZI-seg model has a two-stage architecture using both a "3D transformer for panoptic context-awareness" and a "3D UNet for localized texture refinement." The unique ZOZI strategies for patching were demonstrated in an ablation study. The performance of our proposed ZOZI-seg model was tested using a dataset from Asan Medical Center and compared with those of existing models such as nnUNet and nnFormer. RESULTS In terms of segmentation accuracy, our method yielded better results, with Dice similarity coefficients (DSCs) of 0.917, 0.882, and 0.630 for TL, FL, and TH, respectively. Furthermore, we indirectly compared our model with those in previous studies using an external dataset to evaluate its robustness and generalizability. CONCLUSIONS This approach may help in the diagnosis and treatment of AD in different clinical situations and provide a strong basis for further research and clinical applications.
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Affiliation(s)
- Ji-Hoon Jung
- Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hong Min Oh
- Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Gyu-Jun Jeong
- Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Tae-Won Kim
- Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hyun Jung Koo
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - June-Goo Lee
- Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
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Velikova Y, Simson W, Azampour MF, Paprottka P, Navab N. CACTUSS: Common Anatomical CT-US Space for US examinations. Int J Comput Assist Radiol Surg 2024; 19:861-869. [PMID: 38270811 PMCID: PMC11098881 DOI: 10.1007/s11548-024-03060-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE The detection and treatment of abdominal aortic aneurysm (AAA), a vascular disorder with life-threatening consequences, is challenging due to its lack of symptoms until it reaches a critical size. Abdominal ultrasound (US) is utilized for diagnosis; however, its inherent low image quality and reliance on operator expertise make computed tomography (CT) the preferred choice for monitoring and treatment. Moreover, CT datasets have been effectively used for training deep neural networks for aorta segmentation. In this work, we demonstrate how leveraging CT labels can be used to improve segmentation in ultrasound and hence save manual annotations. METHODS We introduce CACTUSS: a common anatomical CT-US space that inherits properties from both CT and ultrasound modalities to produce an image in intermediate representation (IR) space. CACTUSS acts as a virtual third modality between CT and US to address the scarcity of annotated ultrasound training data. The generation of IR images is facilitated by re-parametrizing a physics-based US simulator. In CACTUSS we use IR images as training data for ultrasound segmentation, eliminating the need for manual labeling. In addition, an image-to-image translation network is employed for the model's application on real B-modes. RESULTS The model's performance is evaluated quantitatively for the task of aorta segmentation by comparison against a fully supervised method in terms of Dice Score and diagnostic metrics. CACTUSS outperforms the fully supervised network in segmentation and meets clinical requirements for AAA screening and diagnosis. CONCLUSION CACTUSS provides a promising approach to improve US segmentation accuracy by leveraging CT labels, reducing the need for manual annotations. We generate IRs that inherit properties from both modalities while preserving the anatomical structure and are optimized for the task of aorta segmentation. Future work involves integrating CACTUSS into robotic ultrasound platforms for automated screening and conducting clinical feasibility studies.
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Affiliation(s)
- Yordanka Velikova
- Computer Aided Medical Procedures, Technical University of Munich, Garching, Germany.
| | - Walter Simson
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mohammad Farid Azampour
- Computer Aided Medical Procedures, Technical University of Munich, Garching, Germany
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, Garching, Germany
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
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Lu X, Gong W, Yang W, Peng Z, Zheng C, Zha Y. Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection. Eur J Radiol 2024; 175:111468. [PMID: 38648727 DOI: 10.1016/j.ejrad.2024.111468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/24/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR). METHODS We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves. RESULTS The integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000-1.000) in the training cohort and 0.990 (95%CI, 0.966-1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965-1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively. CONCLUSIONS The synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.
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Affiliation(s)
- Xuefang Lu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Gong
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wenbing Yang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhoufeng Peng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chao Zheng
- Shukun Technology Co., Ltd, Beichen Century Center, West Beichen Road, 100102 Beijing, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
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Lin W, Gao Z, Liu H, Zhang H. A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1462-1475. [PMID: 38048241 DOI: 10.1109/tmi.2023.3339142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
Aortic segmentation from computed tomography (CT) is crucial for facilitating aortic intervention, as it enables clinicians to visualize aortic anatomy for diagnosis and measurement. However, aortic segmentation faces the challenge of variable geometry in space, as the geometric diversity of different diseases and the geometric transformations that occur between raw and measured images. Existing constraint-based methods can potentially solve the challenge, but they are hindered by two key issues: inaccurate definition of properties and inappropriate topology of transformation in space. In this paper, we propose a deformable constraint transport network (DCTN). The DCTN adaptively extracts aortic features to define intra-image constrained properties and guides topological implementation in space to constrain inter-image geometric transformation between raw and curved planar reformation (CPR) images. The DCTN contains a deformable attention extractor, a geometry-aware decoder and an optimal transport guider. The extractor generates variable patches that preserve semantic integrity and long-range dependency in long-sequence images. The decoder enhances the perception of geometric texture and semantic features, particularly for low-intensity aortic coarctation and false lumen, which removes background interference. The guider explores the geometric discrepancies between raw and CPR images, constructs probability distributions of discrepancies, and matches them with inter-image transformation to guide geometric topology in space. Experimental studies on 267 aortic subjects and four public datasets show the superiority of our DCTN over 23 methods. The results demonstrate DCTN's advantages in aortic segmentation for different types of aortic disease, for different aortic segments, and in the measurement of clinical indexes.
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Garzia S, Scarpolini MA, Mazzoli M, Capellini K, Monteleone A, Cademartiri F, Positano V, Celi S. Coupling synthetic and real-world data for a deep learning-based segmentation process of 4D flow MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107790. [PMID: 37708583 DOI: 10.1016/j.cmpb.2023.107790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/07/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Phase contrast magnetic resonance imaging (4D flow MRI) is an imaging technique able to provide blood velocity in vivo and morphological information. This capability has been used to study mainly the hemodynamics of large vessels, such as the thoracic aorta. However, the segmentation of 4D flow MRI data is a complex and time-consuming task. In recent years, neural networks have shown great accuracy in segmentation tasks if large datasets are provided. Unfortunately, in the context of 4D flow MRI, the availability of these data is limited due to its recent adoption in clinical settings. In this study, we propose a pipeline for generating synthetic thoracic aorta phase contrast magnetic resonance angiography (PCMRA) to expand the limited dataset of patient-specific PCMRA images, ultimately improving the accuracy of the neural network segmentation even with a small real dataset. METHODS The pipeline involves several steps. First, a statistical shape model is used to synthesize new artificial geometries to improve data numerosity and variability. Secondly, computational fluid dynamics simulations are employed to simulate the velocity fields and, finally, after a downsampling and a signal-to-noise and velocity limit adjustment in both frequency and spatial domains, volumes are obtained using the PCMRA formula. These synthesized volumes are used in combination with real-world data to train a 3D U-Net neural network. Different settings of real and synthetic data are tested. RESULTS Incorporating synthetic data into the training set significantly improved the segmentation performance compared to using only real data. The experiments with synthetic data achieved a DICE score (DS) value of 0.83 and a better target reconstruction with respect to the case with only real data (DS = 0.65). CONCLUSION The proposed pipeline demonstrated the ability to increase the dataset in terms of numerosity and variability and to improve the segmentation accuracy for the thoracic aorta using PCMRA.
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Affiliation(s)
- Simone Garzia
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56122, Italy
| | - Martino Andrea Scarpolini
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Industrial Engineering, University of Rome "Tor Vergata", Via del Politecnico, Roma, 00133, Italy
| | - Marilena Mazzoli
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56122, Italy
| | - Katia Capellini
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy
| | - Angelo Monteleone
- Department of Radiology, Fondazione Toscana G Monasterio, Via Moruzzi, Pisa, 56122, Italy
| | - Filippo Cademartiri
- Department of Radiology, Fondazione Toscana G Monasterio, Via Moruzzi, Pisa, 56122, Italy
| | - Vincenzo Positano
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy
| | - Simona Celi
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy.
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Zhang X, Cheng G, Han X, Li S, Xiong J, Wu Z, Zhang H, Chen D. Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning. Phys Med Biol 2023; 68:235011. [PMID: 37774717 DOI: 10.1088/1361-6560/acfec7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/29/2023] [Indexed: 10/01/2023]
Abstract
Objective.Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD.Approach.The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage.Results.The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p< 0.05) and 1.4% (p< 0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921 ± 0.055 versus 0.857 ± 0.220,p< 0.01) and TH (0.811 ± 0.137 versus 0.797 ± 0.146,p< 0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings.Significance.Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.
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Affiliation(s)
- Xuyang Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Guoliang Cheng
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Xiaofeng Han
- Department of Diagnostic and Interventional Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Shilong Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Jiang Xiong
- Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Ziheng Wu
- Department of Vascular Surgery, The First Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Hongkun Zhang
- Department of Vascular Surgery, The First Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
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Bagheri Rajeoni A, Pederson B, Clair DG, Lessner SM, Valafar H. Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning. Diagnostics (Basel) 2023; 13:3363. [PMID: 37958259 PMCID: PMC10649553 DOI: 10.3390/diagnostics13213363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/05/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. Accurate analysis of diagnostic images, like computed tomographic angiograms (CTAs), is essential for staging and monitoring the progression of atherosclerosis-related conditions, including peripheral arterial disease (PAD). However, manual analysis of CTA images is time-consuming and tedious. To address this limitation, we employed a deep learning model to segment the vascular system in CTA images of PAD patients undergoing femoral endarterectomy surgery and to measure vascular calcification from the left renal artery to the patella. Utilizing proprietary CTA images of 27 patients undergoing femoral endarterectomy surgery provided by Prisma Health Midlands, we developed a Deep Neural Network (DNN) model to first segment the arterial system, starting from the descending aorta to the patella, and second, to provide a metric of arterial calcification. Our designed DNN achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%. Furthermore, our work is the first to present a robust statistical analysis of automated calcification measurement in the lower extremities using deep learning, attaining a Mean Absolute Percentage Error (MAPE) of 9.5% and a correlation coefficient of 0.978 between automated and manual calcification scores. These findings underscore the potential of deep learning techniques as a rapid and accurate tool for medical professionals to assess calcification in the abdominal aorta and its branches above the patella.
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Affiliation(s)
- Alireza Bagheri Rajeoni
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA;
| | - Breanna Pederson
- Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USA;
| | - Daniel G. Clair
- Department of Vascular Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - Susan M. Lessner
- Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA;
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Sturla F, Caimi A, Romarowski RM, Nano G, Glauber M, Redaelli A, Votta E, Marrocco-Trischitta MM. Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant. J Endovasc Ther 2023; 30:756-768. [PMID: 35588222 PMCID: PMC10503258 DOI: 10.1177/15266028221095403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Displacement forces (DFs) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of DFs merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure. MATERIALS AND METHODS We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute DFs within the aortic proximal landing zones. To quantify fast-approximate DFs, the wall shear stress contribution to the DF was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, DF values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (EST). RESULTS Fast-approximate approach consistently reflected (r2=0.99, p<0.0001) the DF pattern obtained by CFD, with a -1.1% and 0.7° bias in DFs magnitude and orientation, respectively. The normalized EST progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate EST was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. S showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of DF impact on the thoracic aortic wall. CONCLUSION Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.
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Affiliation(s)
- Francesco Sturla
- 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Alessandro Caimi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Rodrigo M. Romarowski
- 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Giovanni Nano
- Vascular Surgery Unit, Cardiovascular Department, IRCCS Policlinico San Donato, San Donato Milanese, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
| | - Mattia Glauber
- Minimally Invasive Cardiac Surgery Unit, Istituto Clinico Sant’Ambrogio, Milano, Italy
| | - Alberto Redaelli
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Emiliano Votta
- 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Massimiliano M. Marrocco-Trischitta
- Vascular Surgery Unit, Cardiovascular Department, IRCCS Policlinico San Donato, San Donato Milanese, Italy
- Clinical Research Unit, Cardiovascular Department, IRCCS Policlinico San Donato, San Donato Milanese, Italy
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11
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Pepe A, Egger J, Codari M, Willemink MJ, Gsaxner C, Li J, Roth PM, Schmalstieg D, Mistelbauer G, Fleischmann D. Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations. Comput Biol Med 2023; 165:107365. [PMID: 37647783 DOI: 10.1016/j.compbiomed.2023.107365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/20/2023] [Accepted: 08/12/2023] [Indexed: 09/01/2023]
Abstract
Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.
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Affiliation(s)
- Antonio Pepe
- Graz University of Technology, Institute of Computer Graphics and Vision, Inffeldgasse 16/II, 8010 Graz, Austria; Stanford University, School of Medicine, 3D and Quantitative Imaging Lab, 300 Pasteur Drive Stanford, CA 94305, USA; Computer Algorithms for Médicine (Café) Laboratory, Graz, Austria.
| | - Jan Egger
- Computer Algorithms for Médicine (Café) Laboratory, Graz, Austria; University Medicine Essen, Institute for AI in Medicine (IKIM), Girardetstraße 2, 45131 Essen, Germany.
| | - Marina Codari
- Stanford University, School of Medicine, 3D and Quantitative Imaging Lab, 300 Pasteur Drive Stanford, CA 94305, USA.
| | - Martin J Willemink
- Stanford University, School of Medicine, 3D and Quantitative Imaging Lab, 300 Pasteur Drive Stanford, CA 94305, USA.
| | - Christina Gsaxner
- Graz University of Technology, Institute of Computer Graphics and Vision, Inffeldgasse 16/II, 8010 Graz, Austria; Computer Algorithms for Médicine (Café) Laboratory, Graz, Austria.
| | - Jianning Li
- Computer Algorithms for Médicine (Café) Laboratory, Graz, Austria; University Medicine Essen, Institute for AI in Medicine (IKIM), Girardetstraße 2, 45131 Essen, Germany.
| | - Peter M Roth
- Graz University of Technology, Institute of Computer Graphics and Vision, Inffeldgasse 16/II, 8010 Graz, Austria.
| | - Dieter Schmalstieg
- Graz University of Technology, Institute of Computer Graphics and Vision, Inffeldgasse 16/II, 8010 Graz, Austria.
| | - Gabriel Mistelbauer
- Stanford University, School of Medicine, 3D and Quantitative Imaging Lab, 300 Pasteur Drive Stanford, CA 94305, USA.
| | - Dominik Fleischmann
- Stanford University, School of Medicine, 3D and Quantitative Imaging Lab, 300 Pasteur Drive Stanford, CA 94305, USA.
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12
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Akai H, Yasaka K, Sugawara H, Tajima T, Akahane M, Yoshioka N, Ohtomo K, Abe O, Kiryu S. Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study. Magn Reson Med Sci 2023; 22:353-360. [PMID: 35811127 PMCID: PMC10449552 DOI: 10.2463/mrms.mp.2022-0020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 05/22/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR). METHODS Proton density-weighted images of the right knee of 27 healthy volunteers were obtained by 3T and 1.5T MRI scanners using similar imaging parameters (21 for high resolution image and 6 for normal resolution image). Commercially available DLR was applied to the 1.5T images to obtain 1.5T/DLR images. The 3T and 1.5T/DLR images were compared subjectively for visibility of structures, image noise, artifacts, and overall diagnostic acceptability and objectively. One-way ANOVA and Friedman tests were used for the statistical analyses. RESULTS For the high resolution images, all of the anatomical structures, except for bone, were depicted significantly better on the 1.5T/DLR compared with 3T images. Image noise scored statistically lower and overall diagnostic acceptability scored higher on the 1.5T/DLR images. The contrast between lateral meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.89 ± 1.30 vs. 4.34 ± 0.87, P < 0.001), and also the contrast between medial meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.12 ± 0.93 vs. 3.87 ± 0.56, P < 0.001). Similar image quality improvement by DLR was observed for the normal resolution images. CONCLUSION The 1.5T/DLR images can achieve less noise, more precise visualization of the meniscus and ligaments, and higher overall image quality compared with the 3T images acquired using a similar protocol.
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Affiliation(s)
- Hiroyuki Akai
- Department of Radiology, Institute of Medical Science, University of Tokyo, Tokyo, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
| | - Koichiro Yasaka
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Haruto Sugawara
- Department of Radiology, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Taku Tajima
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
- Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
| | - Kuni Ohtomo
- Department of Radiology, International University of Health and Welfare, Ohtawara, Tochigi, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, Narita, Chiba, Japan
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13
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Kesävuori R, Kaseva T, Salli E, Raivio P, Savolainen S, Kangasniemi M. Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection. Eur Radiol Exp 2023; 7:35. [PMID: 37380806 DOI: 10.1186/s41747-023-00342-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/01/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches. METHODS A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively. RESULTS 2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively. CONCLUSIONS Both CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs. RELEVANCE STATEMENT Deep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection. KEY POINTS • 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.
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Affiliation(s)
- Risto Kesävuori
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland.
| | - Tuomas Kaseva
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland
| | - Eero Salli
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland
| | - Peter Raivio
- Department of Cardiac Surgery, Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sauli Savolainen
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland
- Department of Physics, University of Helsinki, Helsinki, Finland
| | - Marko Kangasniemi
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland
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Zhang J, Liu J, Wei S, Chen D, Xiong J, Gao F. Semi-supervised aortic dissections segmentation: A time-dependent weighted feedback fusion framework. Comput Med Imaging Graph 2023; 106:102219. [PMID: 37001423 DOI: 10.1016/j.compmedimag.2023.102219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
The segmentation of true lumen (TL) and false lumen (FL) plays an important role in the diagnosis and treatment of aortic dissection (AD). Although the deep learning methods have achieved remarkable performance for this task, a large number of labeled data are required for training. In order to alleviate the burden of doctors' labeling, in this paper, a novel time-dependent weighted feedback fusion based semi-supervised aortic dissections segmentation framework is proposed by effectively leveraging the unlabeled data. A feedback network is additionally extended to encode the predicted output from the backbone network into high-level feature space, which is then fused with the original feature information of the image to fix previous potential mistakes, thereby segmentation accuracy is improved iteratively. To utilize both labeled data and unlabeled data, the fused feature space flows into the network again to generate the second feedback and make sure consistency with the previous one. The utilization of image feature space provides better robustness and accuracy for the proposed structure. Experiments show that our method outperforms five existing state-of-the-art semi-supervised segmentation methods on both a type-B AD dataset and a public dataset.
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Affiliation(s)
- Jinhui Zhang
- School of Automation, Beijing Institute of Technology, Beijing 100081, China.
| | - Jian Liu
- School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Siyi Wei
- School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jiang Xiong
- Department of Vascular and Endovascular Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Department of Vascular and Endovascular Surgery, Hainan Hospital, Chinese PLA General Hospital, Hainan 572013, China
| | - Feng Gao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
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15
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Xiang D, Qi J, Wen Y, Zhao H, Zhang X, Qin J, Ma X, Ren Y, Hu H, Liu W, Yang F, Zhao H, Wang X, Zheng C. ADSeg: A flap-attention-based deep learning approach for aortic dissection segmentation. PATTERNS (NEW YORK, N.Y.) 2023; 4:100727. [PMID: 37223272 PMCID: PMC10201300 DOI: 10.1016/j.patter.2023.100727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/16/2023] [Accepted: 03/14/2023] [Indexed: 05/25/2023]
Abstract
Accurate and rapid segmentation of the lumen in an aortic dissection (AD) is an important prerequisite for risk evaluation and medical planning for patients with this serious condition. Although some recent studies have pioneered technical advances for the challenging AD segmentation task, they generally neglect the intimal flap structure that separates the true and false lumens. Identification and segmentation of the intimal flap may simplify AD segmentation, and the incorporation of long-distance z axis information interaction along the curved aorta may improve segmentation accuracy. This study proposes a flap attention module that focuses on key flap voxels and performs operations with long-distance attention. In addition, a pragmatic cascaded network structure with feature reuse and a two-step training strategy are presented to fully exploit network representation power. The proposed ADSeg method was evaluated on a multicenter dataset of 108 cases, with or without thrombus; ADSeg outperformed previous state-of-the-art methods by a significant margin and was robust against center variation.
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Affiliation(s)
- Dongqiao Xiang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Jiyang Qi
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yiqing Wen
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Zhao
- Department of Interventional Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xiaolin Zhang
- Department of Radiology, Yichang Central People’s Hospital, Yichang 443003, China
| | - Jia Qin
- Department of Radiology, Yichang Central People’s Hospital, Yichang 443003, China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei province, Jingzhou 434000, China
| | - Yaguang Ren
- Research Laboratory for Biomedical Optics and Molecular Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Hongyao Hu
- Department of Interventional Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Wenyu Liu
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Huangxuan Zhao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xinggang Wang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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16
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Yang J, Li X, Cheng JZ, Xue Z, Shi F, Ji Y, Wang X, Yang F. Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease. Comput Biol Med 2023; 160:107002. [PMID: 37187136 DOI: 10.1016/j.compbiomed.2023.107002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/29/2023] [Accepted: 05/02/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Non-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians' experience. PURPOSE The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology. METHODS The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning. RESULTS Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases. CONCLUSION We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.
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Affiliation(s)
- Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiang Li
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Jie-Zhi Cheng
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Zhong Xue
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Yuqing Ji
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Xuechun Wang
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China.
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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Diao K, Liang HQ, Yin HK, Yuan MJ, Gu M, Yu PX, He S, Sun J, Song B, Li K, He Y. Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH. Insights Imaging 2023; 14:70. [PMID: 37093501 PMCID: PMC10126185 DOI: 10.1186/s13244-023-01401-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images. METHODS A total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists. RESULTS Model 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895-0.980, 0.879-0.984 and 0.848-0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists. CONCLUSION The combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hong-Qing Liang
- Department of Radiology, First Affiliated Hospital to Army Medical University (Third Military Medical University Southwest Hospital), Chongqing, China
| | - Hong-Kun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Ming-Jing Yuan
- Department of Radiology, Yongchuan Hospital, Chongqing Medical University, Chongqing, China
| | - Min Gu
- Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing, China
| | - Peng-Xin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Sen He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Jiayu Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, Sanya Municipal People's Hospital, Sanya, Hainan, China
| | - Kang Li
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Yong He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
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18
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Chen PH, Nakamura K, Obuchowski N, Juan MCY, Zhang S, Flamm SD, Desai MY, Hovest T, Meese T, Schoenhagen P. Identification of acute aortic syndromes based on cross-sectional variability of Hounsfield units. Int J Cardiol 2023; 382:91-95. [PMID: 37080465 DOI: 10.1016/j.ijcard.2023.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/17/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND A characteristic feature of communicating aortic dissections (CD) is the dissection flap between the true and false lumen. However, in intramural hematomas (IMH) a flap is not visible. We aimed to determine if cross-sectional HU variability allow reliable identification of aortic dissections including IMH. METHODS We included 362 patients presenting with acute chest pain (CP) or respiratory distress (RD) and underwent contrast-enhanced CTA with or without ECG-gating. In the derivation group we included 72 CP patients with and 74 without AAS. In the validation group we included 108 CP or RD patients with and 108 without AAS. The adventitial border of the aorta was visually identified and measurements were performed at 6 locations along the ascending and descending aorta. At each cross-section 5 circular ROI measurements of HU were made and the maximum HU difference calculated. RESULTS In the derivation and validation group the maximum difference in HUs at any one location was significantly higher for AAS subjects than controls (validation group: median = 128.5 vs. 34.0, p-value Wilcoxon two-sample test <0.001). In the validation group, the estimated AUC was 0.939 with 95% CIs of [0.906, 0.972], indicating that the maximum difference in HUs is a strong predictor of AAS (p < 0.001). CONCLUSION Our data provide evidence that cross-sectional variability of Hounsfield Unit reliably identifies aortic dissection including IMH in dedicated ECG-gated aorta scans but also non-gated chest CTs with limited aortic contrast enhancement. These results suggest that this approach could be feasible for an automated algorithm for identification of AAS.
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Affiliation(s)
- Po-Hao Chen
- Cleveland Clinic, Imaging Institute, Cleveland, OH, USA
| | - Kunio Nakamura
- Cleveland Clinic, Department of Biomedical Engineering, Cleveland, OH, USA
| | - Nancy Obuchowski
- Cleveland Clinic, Department of Quantitative Health Sciences, Cleveland, OH, USA
| | | | | | - Scott D Flamm
- Cleveland Clinic, Imaging Institute, Cleveland, OH, USA; Cleveland Clinic, Heart, Vascular & Thoracic Institute, Cleveland, OH, USA
| | - Milind Y Desai
- Cleveland Clinic, Imaging Institute, Cleveland, OH, USA; Cleveland Clinic, Heart, Vascular & Thoracic Institute, Cleveland, OH, USA
| | - Torey Hovest
- Cleveland Clinic, Innovations, Cleveland Clinic, Cleveland, OH, USA
| | - Thad Meese
- Cleveland Clinic, Innovations, Cleveland Clinic, Cleveland, OH, USA
| | - Paul Schoenhagen
- Cleveland Clinic, Imaging Institute, Cleveland, OH, USA; Cleveland Clinic, Heart, Vascular & Thoracic Institute, Cleveland, OH, USA.
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19
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Zhou M, Luo X, Wang X, Xie T, Wang Y, Shi Z, Wang M, Fu W. Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection. J Endovasc Ther 2023:15266028231160101. [PMID: 36927177 DOI: 10.1177/15266028231160101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
PURPOSE This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA). METHODS A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set. RESULTS The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81). CONCLUSION The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD. CLINICAL IMPACT Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.
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Affiliation(s)
- Min Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xiaoyuan Luo
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Xia Wang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yonggang Wang
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Manning Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
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20
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Joshi M, Melo DP, Ouyang D, Slomka PJ, Williams MC, Dey D. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep 2023; 25:109-117. [PMID: 36708505 DOI: 10.1007/s11886-022-01837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. RECENT FINDINGS Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.
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Affiliation(s)
- Mugdha Joshi
- Department of Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - Diana Patricia Melo
- Division of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - David Ouyang
- Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA.
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21
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Feng H, Fu Z, Wang Y, Zhang P, Lai H, Zhao J. Automatic segmentation of thrombosed aortic dissection in post-operative CT-angiography images. Med Phys 2022. [PMID: 36542417 DOI: 10.1002/mp.16169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/02/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The thrombus in the false lumen (FL) of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the FL in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy. METHODS A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement (SAR) modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features. RESULTS The proposed method provided accurate thrombus segmentation results (0.903 ± 0.062 in dice score, 0.828 ± 0.092 in Jaccard index, and 2.209 ± 2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846 ± 0.085 in dice score) and the method without SAR (0.899 ± 0.060 in dice score). Moreover, the proposed method achieved 0.967 ± 0.029 and 0.948 ± 0.041 in dice score of true lumen (TL) and patent FL (PFL) segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta. CONCLUSIONS A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application.
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Affiliation(s)
- Hanying Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zheng Fu
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Yulin Wang
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hao Lai
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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22
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Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet). Sci Rep 2022; 12:21884. [PMID: 36536152 PMCID: PMC9763432 DOI: 10.1038/s41598-022-26486-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.
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23
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Mastrodicasa D, Codari M, Bäumler K, Sandfort V, Shen J, Mistelbauer G, Hahn LD, Turner VL, Desjardins B, Willemink MJ, Fleischmann D. Artificial Intelligence Applications in Aortic Dissection Imaging. Semin Roentgenol 2022; 57:357-363. [PMID: 36265987 PMCID: PMC10013132 DOI: 10.1053/j.ro.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/25/2022] [Accepted: 07/02/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Domenico Mastrodicasa
- Department of Radiology, Stanford University School of Medicine, Stanford, CA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA.
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Kathrin Bäumler
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Veit Sandfort
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Jody Shen
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Gabriel Mistelbauer
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Lewis D Hahn
- University of California San Diego, Department of Radiology, La Jolla, CA
| | - Valery L Turner
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Benoit Desjardins
- Department of Radiology, Stanford University School of Medicine, Stanford, CA; Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | - Martin J Willemink
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Dominik Fleischmann
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
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24
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Zhao J, Zhao J, Pang S, Feng Q. Segmentation of the True Lumen of Aorta Dissection via Morphology-Constrained Stepwise Deep Mesh Regression. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1826-1836. [PMID: 35133960 DOI: 10.1109/tmi.2022.3150005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The lumen of aortic dissection (AD) has important clinical value for preoperative diagnosis, interoperative intervention, and post-operative evaluation of AD diseases. AD segmentation is challenging because (i) fitting its irregular profile by using traditional models is difficult, and (ii) the size of the AD image is usually so big that many algorithms have to perform down-sampling to reduce the computational burden, thereby reducing the resolution of the result. In this paper, an automatic AD segmentation algorithm, in which a 3D mesh is gradually moved to the surface of AD based on the offset estimated by a deep mesh deformation module, is presented. AD morphology is used to constrain the initial mesh and guide the deformation, which improves the efficiency of the deep network and avoids down-sampling. Moreover, a stepwise regression strategy is introduced to solve the mesh folding problem and improve the uniformity of the mesh points. On an AD database that involves 35 images, the proposed method obtains the mean Dice of 94.12% and symmetric 95% Hausdorff distance of 2.85 mm, which outperforms five state-of-the-art AD segmentation methods. The average processing time is 16.6 s, and the memory used to train the network is only 0.36 GB, indicating that this method is easy to apply in clinical practice.
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25
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Kamada H, Nakamura M, Ota H, Higuchi S, Takase K. Blood flow analysis with computational fluid dynamics and 4D-flow MRI for vascular diseases. J Cardiol 2022; 80:386-396. [PMID: 35718672 DOI: 10.1016/j.jjcc.2022.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 10/31/2022]
Abstract
Both computational fluid dynamics (CFD) and time-resolved, three-dimensional, phase-contrast, magnetic resonance imaging (4D-flow MRI) enable visualization of time-varying blood flow structures and quantification of blood flow in vascular diseases. However, they are totally different. CFD is a method to calculate blood flow by solving the governing equations of fluid mechanics, so the obtained flow field is somewhat virtual. On the other hand, 4D-flow MRI measures blood flow in vivo, thus the flow is real. Recently, with the development and enhancement of computers, medical imaging techniques, and related software, blood flow analysis has become more accessible to clinicians and its usefulness in vascular diseases has been demonstrated. In this review, we have outlined the methods and characteristics of CFD and 4D-flow MRI, respectively. We have discussed the differences in the characteristics between both methods; reviewed the milestones achieved by blood flow analysis in various vascular diseases; and discussed the usefulness, challenges, and limitations of blood flow analysis. We have discussed the difficulties and limitations of current blood flow analysis. We have also discussed our views on future directions.
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Affiliation(s)
- Hiroki Kamada
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan.
| | - Masanori Nakamura
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Hideki Ota
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan
| | - Satoshi Higuchi
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan
| | - Kei Takase
- Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan
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26
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Diao K, Chen Y, Liu Y, Chen BJ, Li WJ, Zhang L, Qu YL, Zhang T, Zhang Y, Wu M, Li K, Song B. Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:668. [PMID: 35845492 PMCID: PMC9279799 DOI: 10.21037/atm-22-2157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers’ final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2–98.0%] and a positive predictive value of 55.6% (95% CI: 49.0–62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist’s decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9–732.5) vs. 141.3 (79.3–380.8) mm3, P<0.001], lower average CT number [−511.0 (−576.5 to −100.5) vs. −191.5 (−487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo-Jiang Chen
- Department of Respiratory Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Wan-Jiang Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ya-Li Qu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital (West China Sanya Hospital of Sichuan University), Chengdu, China
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27
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Fleischmann D, Afifi RO, Casanegra AI, Elefteriades JA, Gleason TG, Hanneman K, Roselli EE, Willemink MJ, Fischbein MP. Imaging and Surveillance of Chronic Aortic Dissection: A Scientific Statement From the American Heart Association. Circ Cardiovasc Imaging 2022; 15:e000075. [PMID: 35172599 DOI: 10.1161/hci.0000000000000075] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
All patients surviving an acute aortic dissection require continued lifelong surveillance of their diseased aorta. Late complications, driven predominantly by chronic false lumen degeneration and aneurysm formation, often require surgical, endovascular, or hybrid interventions to treat or prevent aortic rupture. Imaging plays a central role in the medical decision-making of patients with chronic aortic dissection. Accurate aortic diameter measurements and rigorous, systematic documentation of diameter changes over time with different imaging equipment and modalities pose a range of practical challenges in these complex patients. Currently, no guidelines or recommendations for imaging surveillance in patients with chronic aortic dissection exist. In this document, we present state-of-the-art imaging and measurement techniques for patients with chronic aortic dissection and clarify the need for standardized measurements and reporting for lifelong surveillance. We also examine the emerging role of imaging and computer simulations to predict aortic false lumen degeneration, remodeling, and biomechanical failure from morphological and hemodynamic features. These insights may improve risk stratification, individualize contemporary treatment options, and potentially aid in the conception of novel treatment strategies in the future.
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28
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Abstract
Positron emission tomography (PET) offers an incredible wealth of diverse research applications in vascular disease, providing a depth of molecular, functional, structural, and spatial information. Despite this, vascular PET imaging has not yet assumed the same clinical use as vascular ultrasound, CT, and MR imaging which provides information about late-onset, structural tissue changes. The current clinical utility of PET relies heavily on visual inspection and suboptimal parameters such as SUVmax; emerging applications have begun to harness the tool of whole-body PET to better understand the disease. Even still, without automation, this is a time-consuming and variable process. This review summarizes PET applications in vascular disorders, highlights emerging AI methods, and discusses the unlocked potential of AI in the clinical space.
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29
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Golla AK, Tönnes C, Russ T, Bauer DF, Froelich MF, Diehl SJ, Schoenberg SO, Keese M, Schad LR, Zöllner FG, Rink JS. Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning. Diagnostics (Basel) 2021; 11:2131. [PMID: 34829478 PMCID: PMC8621263 DOI: 10.3390/diagnostics11112131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/10/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022] Open
Abstract
Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.
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Affiliation(s)
- Alena-K. Golla
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Christian Tönnes
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Tom Russ
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Dominik F. Bauer
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Steffen J. Diehl
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Michael Keese
- Department of Surgery, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany;
| | - Lothar R. Schad
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Frank G. Zöllner
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Johann S. Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
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30
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Wobben LD, Codari M, Mistelbauer G, Pepe A, Higashigaito K, Hahn LD, Mastrodicasa D, Turner VL, Hinostroza V, Baumler K, Fischbein MP, Fleischmann D, Willemink MJ. Deep Learning-Based 3D Segmentation of True Lumen, False Lumen, and False Lumen Thrombosis in Type-B Aortic Dissection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3912-3915. [PMID: 34892087 PMCID: PMC9261941 DOI: 10.1109/embc46164.2021.9631067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Patients with initially uncomplicated typeB aortic dissection (uTBAD) remain at high risk for developing late complications. Identification of morphologic features for improving risk stratification of these patients requires automated segmentation of computed tomography angiography (CTA) images. We developed three segmentation models utilizing a 3D residual U-Net for segmentation of the true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT). Model 1 segments all labels at once, whereas model 2 segments them sequentially. Best results for TL and FL segmentation were achieved by model 2, with median (interquartiles) Dice similarity coefficients (DSC) of 0.85 (0.77-0.88) and 0.84 (0.82-0.87), respectively. For FLT segmentation, model 1 was superior to model 2, with median (interquartiles) DSCs of 0.63 (0.40-0.78). To purely test the performance of the network to segment FLT, a third model segmented FLT starting from the manually segmented FL, resulting in median (interquartiles) DSCs of 0.99 (0.98-0.99) and 0.85 (0.73-0.94) for patent FL and FLT, respectively. While the ambiguous appearance of FLT on imaging remains a significant limitation for accurate segmentation, our pipeline has the potential to help in segmentation of aortic lumina and thrombosis in uTBAD patients.Clinical relevance- Most predictors of aortic dissection (AD) degeneration are identified through anatomical modeling, which is currently prohibitive in clinical settings due to the timeintense human interaction. False lumen thrombosis, which often develops in patients with type B AD, has proven to show significant prognostic value for predicting late adverse events. Our automated segmentation algorithm offers the potential of personalized treatment for AD patients, leading to an increase in long-term survival.
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31
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Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021; 59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.
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Affiliation(s)
- Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA.
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32
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Abstract
The automatic segmentation of the aorta can be extremely useful in clinical practice, allowing the diagnosis of numerous pathologies to be sped up, such as aneurysms and dissections, and allowing rapid reconstructive surgery, essential in saving patients’ lives. In recent years, the success of Deep Learning (DL)-based decision support systems has increased their popularity in the medical field. However, their effective application is often limited by the scarcity of training data. In fact, collecting large annotated datasets is usually difficult and expensive, especially in the biomedical domain. In this paper, an automatic method for aortic segmentation, based on 2D convolutional neural networks (CNNs), using 3D CT (computed axial tomography) scans as input is presented. For this purpose, a set of 153 CT images was collected and a semi-automated approach was used to obtain their 3D annotations at the voxel level. Although less accurate, the use of a semi-supervised labeling technique instead of a full supervision proved necessary to obtain enough data in a reasonable amount of time. The 3D volume was analyzed using three 2D segmentation networks, one for each of the three CT views (axial, coronal and sagittal). Two different network architectures, U-Net and LinkNet, were used and compared. The main advantages of the proposed method lie in its ability to work with a reduced number of data even with noisy targets. In addition, analyzing 3D scans based on 2D slices allows for them to be processed even with limited computing power. The results obtained are promising and show that the neural networks employed can provide accurate segmentation of the aorta.
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Sedghi Gamechi Z, Arias-Lorza AM, Saghir Z, Bos D, de Bruijne M. Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts. Med Phys 2021; 48:7837-7849. [PMID: 34653274 PMCID: PMC9298252 DOI: 10.1002/mp.15289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/24/2021] [Accepted: 09/09/2021] [Indexed: 01/29/2023] Open
Abstract
Purpose Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph‐cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans. Methods The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi‐atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in three planes. The centerlines are refined by applying the path tracking algorithm to curved multiplanar reformatted scans and are then smoothed and dilated nonuniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph‐cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the PA:AA ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted PA bifurcation level. The method is evaluated on noncontrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intraclass correlation (ICC) between manual and automatic diameters, both measured in axial slices at the PA bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and PA:AA ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan–rescan pairs with an average in‐between time of 3 months. Results We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in‐slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the PA:AA ratio, and for automatic diameters in 3D volumes around the PA bifurcation level between scan and rescan was 0.89, 0.95, and 0.86, respectively. Conclusion The proposed automatic segmentation method can reliably extract diameters of the large arteries in non‐ECG‐gated noncontrast CT scans such as are acquired in lung cancer screening.
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Affiliation(s)
- Zahra Sedghi Gamechi
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Andres M Arias-Lorza
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Zaigham Saghir
- Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Yao Z, Xie W, Zhang J, Dong Y, Qiu H, Yuan H, Jia Q, Wang T, Shi Y, Zhuang J, Que L, Xu X, Huang M. ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection. Front Physiol 2021; 12:732711. [PMID: 34646158 PMCID: PMC8503642 DOI: 10.3389/fphys.2021.732711] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public (Dataset, 2020).
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Affiliation(s)
- Zeyang Yao
- School of Medicine, South China University of Technology, Guangzhou, China.,Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wen Xie
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jiawei Zhang
- School of Computer Science, Fudan University, Shanghai, China
| | - Yuhao Dong
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hailong Qiu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Haiyun Yuan
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qianjun Jia
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Tianchen Wang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Yiyi Shi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Jian Zhuang
- School of Medicine, South China University of Technology, Guangzhou, China.,Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lifeng Que
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Xiaowei Xu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Meiping Huang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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35
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Zhao J, Feng Q. Automatic Aortic Dissection Centerline Extraction Via Morphology-Guided CRN Tracker. IEEE J Biomed Health Inform 2021; 25:3473-3485. [PMID: 33755572 DOI: 10.1109/jbhi.2021.3068420] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Aortic dissection (AD) centerline extraction has important clinical value in the quantitative diagnosis and treatment of AD disease. However, AD centerline extraction is a difficult task and quantitative evaluation is rarely studied. In this work, we propose a fully automatic algorithm to extract AD centerline based on a convolutional regression network (CRN) and the morphological properties of AD. To this end, we first design a topological model to describe the complex topology of AD. With this model, CRNs are trained to estimate the position, tangential vector, and scale of the centerline. The tracking accuracy is further improved by centerline continuity and a gradient-based penalty function. In addition, seed points are extracted on the basis of random regression and line clustering to ensure automated vessel tracking. The proposed method has been evaluated on an AD database and a public aortic database, and achieved high overlapping ratios of 0.9610 and 1.0000, respectively. The tracked centerline is very close to the ground truth and shows good stability, with low average distance errors of 1.4720 mm and 1.8748 mm, respectively.
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36
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Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning. J Clin Med 2021; 10:jcm10153347. [PMID: 34362129 PMCID: PMC8347188 DOI: 10.3390/jcm10153347] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.
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37
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Yang DH. Application of Artificial Intelligence to Cardiovascular Computed Tomography. Korean J Radiol 2021; 22:1597-1608. [PMID: 34402240 PMCID: PMC8484158 DOI: 10.3348/kjr.2020.1314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/26/2021] [Accepted: 05/14/2021] [Indexed: 11/15/2022] Open
Abstract
Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.
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Affiliation(s)
- Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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38
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Danilov VV, Klyshnikov KY, Gerget OM, Skirnevsky IP, Kutikhin AG, Shilov AA, Ganyukov VI, Ovcharenko EA. Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning. Front Cardiovasc Med 2021; 8:697737. [PMID: 34350220 PMCID: PMC8326378 DOI: 10.3389/fcvm.2021.697737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/10/2021] [Indexed: 11/15/2022] Open
Abstract
Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.
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Affiliation(s)
- Viacheslav V. Danilov
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Kirill Yu. Klyshnikov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Olga M. Gerget
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Igor P. Skirnevsky
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Anton G. Kutikhin
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Aleksandr A. Shilov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Vladimir I. Ganyukov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Evgeny A. Ovcharenko
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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39
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Kurata Y, Nishio M, Moribata Y, Kido A, Himoto Y, Otani S, Fujimoto K, Yakami M, Minamiguchi S, Mandai M, Nakamoto Y. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 2021; 11:14440. [PMID: 34262088 PMCID: PMC8280152 DOI: 10.1038/s41598-021-93792-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/29/2021] [Indexed: 12/29/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57-0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.
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Affiliation(s)
- Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan.
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan.
| | - Yusaku Moribata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
- Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Aki Kido
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Satoshi Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Koji Fujimoto
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Masahiro Yakami
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
- Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Sachiko Minamiguchi
- Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
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Sieren MM, Widmann C, Weiss N, Moltz JH, Link F, Wegner F, Stahlberg E, Horn M, Oecherting TH, Goltz JP, Barkhausen J, Frydrychowicz A. Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach. Eur Radiol 2021; 32:690-701. [PMID: 34170365 DOI: 10.1007/s00330-021-08130-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/26/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To develop and validate a deep learning-based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies. METHODS CTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators. A 3D U-Net model was trained on the data, which was divided into a training, a validation, and a test group at a ratio of 7:1:2. Cases in the test group (n = 41) were evaluated to compare manual and automatic segmentations. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were extracted. Maximum diameter, effective diameter, and area were quantified and compared between both segmentations at eight anatomical landmarks, and at the maximum area of an aneurysms if present (n = 14). Statistics included error calculation, intraclass correlation coefficient, and Bland-Altman analysis. RESULTS A DSC of 0.95 [0.94; 0.95] and an MSD of 0.76 [0.06; 0.99] indicated close agreement between segmentations. HSD was 8.00 [4.47; 10.00]. The largest absolute errors were found in the ascending aorta with 0.8 ± 1.5 mm for maximum diameter and at the coeliac trunk with - 30.0 ± 81.6 mm2 for area. Results for absolute errors in aneurysms were - 0.5 ± 2.3 mm for maximum diameter, 0.3 ± 1.6 mm for effective diameter, and 64.9 ± 114.9 mm2 for area. ICC showed excellent agreement (> 0.9; p < 0.05) between quantitative measurements. CONCLUSIONS Automated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and allows for accurate quantification of the aortic lumen even if the vascular architecture is altered by disease. KEY POINTS • A deep learning-based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease. • Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta.
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Affiliation(s)
- Malte Maria Sieren
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Cornelia Widmann
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Nick Weiss
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck/Bremen, Germany
| | - Jan Hendrik Moltz
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck/Bremen, Germany
| | - Florian Link
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck/Bremen, Germany
| | - Franz Wegner
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Erik Stahlberg
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Marco Horn
- Department for Vascular Surgery, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Thekla Helene Oecherting
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Jan Peter Goltz
- Institute for Diagnostic and Interventional Radiology/Neuroradiology, Sana Clinic, Lübeck, Germany
| | - Joerg Barkhausen
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Alex Frydrychowicz
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
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Chen D, Zhang X, Mei Y, Liao F, Xu H, Li Z, Xiao Q, Guo W, Zhang H, Yan T, Xiong J, Ventikos Y. Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification. Med Image Anal 2020; 69:101931. [PMID: 33618153 DOI: 10.1016/j.media.2020.101931] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 11/20/2020] [Accepted: 11/27/2020] [Indexed: 12/30/2022]
Abstract
Aortic dissection (AD) is a life-threatening cardiovascular disease with a high mortality rate. The accurate and generalized 3-D reconstruction of AD from CT-angiography can effectively assist clinical procedures and surgery plans, however, is clinically unavaliable due to the lacking of efficient tools. In this study, we presented a novel multi-stage segmentation framework for type B AD to extract true lumen (TL), false lumen (FL) and all branches (BR) as different classes. Two cascaded neural networks were used to segment the aortic trunk and branches and to separate the dual lumen, respectively. An aortic straightening method was designed based on the prior vascular anatomy of AD, simplifying the curved aortic shape before the second network. The straightening-based method achieved the mean Dice scores of 0.96, 0.95 and 0.89 for TL, FL, and BR on a multi-center dataset involving 120 patients, outperforming the end-to-end multi-class methods and the multi-stage methods without straightening on the dual-lumen segmentation, even using different network architectures. Both the global volumetric features of the aorta and the local characteristics of the primary tear could be better identified and quantified based on the straightening. Comparing to previous deep learning methods dealing with AD segmentations, the proposed framework presented advantages in segmentation accuracy.
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Affiliation(s)
- Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China.
| | - Xuyang Zhang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yuqian Mei
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Fangzhou Liao
- Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
| | - Huanming Xu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Zhenfeng Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Qianjiang Xiao
- Shukun (Beijing) Network Technology Co.Ltd., Beijing, China
| | - Wei Guo
- Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, China
| | - Hongkun Zhang
- Department of Vascular Surgery, First Affiliated Hospital of Medical College, Zhejiang University, Hangzhou, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.
| | - Jiang Xiong
- Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, China.
| | - Yiannis Ventikos
- Department of Mechanical Engineering, University College London, London, UK; School of Life Science, Beijing Institute of Technology, Beijing, China
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42
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Yu Y, Gao Y, Wei J, Liao F, Xiao Q, Zhang J, Yin W, Lu B. A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection. Korean J Radiol 2020; 22:168-178. [PMID: 33236538 PMCID: PMC7817629 DOI: 10.3348/kjr.2020.0313] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 05/16/2020] [Accepted: 05/24/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). MATERIALS AND METHODS Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. RESULTS The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). CONCLUSION The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.
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Affiliation(s)
- Yitong Yu
- Department of Radiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences; State Key Lab and National Center for Cardiovascular Diseases, Beijng, China
| | - Yang Gao
- Department of Radiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences; State Key Lab and National Center for Cardiovascular Diseases, Beijng, China
| | - Jianyong Wei
- ShuKun (BeiJing) Technology Co., Ltd., Beijing, China
| | - Fangzhou Liao
- Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
| | | | - Jie Zhang
- Department of Radiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences; State Key Lab and National Center for Cardiovascular Diseases, Beijng, China
| | - Weihua Yin
- Department of Radiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences; State Key Lab and National Center for Cardiovascular Diseases, Beijng, China
| | - Bin Lu
- Department of Radiology, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences; State Key Lab and National Center for Cardiovascular Diseases, Beijng, China.
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Kaschwich M, Horn M, Matthiensen S, Stahlberg E, Behrendt CA, Matysiak F, Bouchagiar J, Dell A, Ellebrecht D, Bayer A, Kleemann M. Accuracy evaluation of patient-specific 3D-printed aortic anatomy. Ann Anat 2020; 234:151629. [PMID: 33137459 DOI: 10.1016/j.aanat.2020.151629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 11/20/2022]
Abstract
INTRODUCTION 3D printing has a wide range of applications in medicine. In surgery, this technique can be used for preoperative planning of complex procedures, production of patient specific implants, as well as training. However, accuracy evaluations of 3D vascular models are rare. OBJECTIVES Aim of this study was to investigate the accuracy of patient-specific 3D-printed aortic anatomies. METHODS Patients suffering from aorto-iliac aneurysms and with indication for treatment were selected on the basis of different anatomy and localization of the aneurysm in the period from January 1st 2014 to May 27th 2016. Six patients with aorto-iliac aneurysms were selected out of the database for 3D-printing. Subsequently, computed tomography (CT) images of the printed 3D-models were compared with the original CT data sets. RESULTS The mean deviation of the six 3D-vascular models ranged between -0.73 mm and 0.14 mm compared to the original CT-data. The relative deviation of the measured values showed no significant difference between the 3D-vascular and the original patient CT-data. CONCLUSION Our results showed that 3D printing has the potential to produce patient-specific 3D vascular models with reliable accuracy. This enables the use of such models for the development of new endovascular procedures and devices.
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Affiliation(s)
- Mark Kaschwich
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Department of Vascular Medicine, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Marco Horn
- Department of Surgery, Division of Vascular and Endovascular Surgery, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Sarah Matthiensen
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Erik Stahlberg
- Department for Radiology and Nuclear Medicine, University Hospital of Schleswig-Holstein, Campus Lübeck, Germany
| | - Christian-Alexander Behrendt
- Department of Vascular Medicine, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Florian Matysiak
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Juljan Bouchagiar
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Annika Dell
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | | | - Andreas Bayer
- Institute of Anatomy, Christian-Albrechts University of Kiel, Kiel, Germany
| | - Markus Kleemann
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Kliniken Dr. Erler, 90429 Nürnberg, Germany
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Fully Automated Segmentation and Shape Analysis of the Thoracic Aorta in Non-contrast-enhanced Magnetic Resonance Images of the German National Cohort Study. J Thorac Imaging 2020; 35:389-398. [PMID: 32349056 DOI: 10.1097/rti.0000000000000522] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study was to develop and validate a deep learning-based framework for automated segmentation and vessel shape analysis on non-contrast-enhanced magnetic resonance (MR) data of the thoracic aorta within the German National Cohort (GNC) MR study. MATERIALS AND METHODS One hundred data sets acquired in the GNC MR study were included (56 men, average age 53 y [22 to 72 y]). All participants had undergone non-contrast-enhanced MR imaging of the thoracic vessels. Automated vessel segmentation of the thoracic aorta was performed using a Convolutional Neural Network in a supervised setting with manually annotated data sets as the ground truth. Seventy data sets were used for training; 30 data sets were used for quantitative and qualitative evaluation. Automated shape analysis based on centerline extraction from segmentation masks was performed to derive a diameter profile of the vessel. For comparison, 2 radiologists measured vessel diameters manually. RESULTS Overall, automated aortic segmentation was successful, providing good qualitative analyses with only minor irregularities in 29 of 30 data sets. One data set with severe MR artifacts led to inadequate automated segmentation results. The mean Dice score of automated vessel segmentation was 0.85. Automated aortic diameter measurements were similar to manual measurements (average difference -0.9 mm, limits of agreement: -5.4 to 3.9 mm), with minor deviations in the order of the interreader agreement between the 2 radiologists (average difference -0.5 mm, limits of agreement: -5.8 to 4.8 mm). CONCLUSION Automated segmentation and shape analysis of the thoracic aorta is feasible with high accuracy on non-contrast-enhanced MR imaging using the proposed deep learning approach.
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Zheng QY, Zhang GQ. Application of leukocyte esterase strip test in the screening of periprosthetic joint infections and prospects of high-precision strips. ARTHROPLASTY 2020; 2:34. [PMID: 35236471 PMCID: PMC8796411 DOI: 10.1186/s42836-020-00053-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 10/12/2020] [Indexed: 12/19/2022] Open
Abstract
Periprosthetic joint infection (PJI) represents one of the most challenging complications after total joint arthroplasty (TJA). Despite the availability of a variety of diagnostic techniques, the diagnosis of PJI remains a challenge due to the lack of well-established diagnostic criteria. The leucocyte esterase (LE) strips test has been proved to be a valuable diagnostic tool for PJI, and its weight in PJI diagnostic criteria has gradually increased. Characterized by its convenience, speed and immediacy, leucocyte esterase strips test has a prospect of broad application in PJI diagnosis. Admittedly, the leucocyte esterase strips test has some limitations, such as imprecision and liability to interference. Thanks to the application of new technologies, such as machine reading, quantitative detection and artificial intelligence, the LE strips test is expected to overcome the limitations and improve its accuracy.
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Wheatley GH. Rise of the Machines: The Evolution of Cardiovascular Imaging for Aortic Disease. INNOVATIONS-TECHNOLOGY AND TECHNIQUES IN CARDIOTHORACIC AND VASCULAR SURGERY 2020; 15:502-505. [DOI: 10.1177/1556984520963644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Pepe A, Li J, Rolf-Pissarczyk M, Gsaxner C, Chen X, Holzapfel GA, Egger J. Detection, segmentation, simulation and visualization of aortic dissections: A review. Med Image Anal 2020; 65:101773. [DOI: 10.1016/j.media.2020.101773] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/01/2020] [Accepted: 07/06/2020] [Indexed: 12/16/2022]
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Fantazzini A, Esposito M, Finotello A, Auricchio F, Pane B, Basso C, Spinella G, Conti M. 3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks. Cardiovasc Eng Technol 2020; 11:576-586. [PMID: 32783134 PMCID: PMC7511465 DOI: 10.1007/s13239-020-00481-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence. METHODS A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence. RESULTS The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation. CONCLUSION The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.
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Affiliation(s)
- Alice Fantazzini
- Department of Experimental Medicine, University of Genoa, Via Leon Battista Alberti, 2, 16132, Genoa, Italy.
- Camelot Biomedical Systems S.r.l, Via Al Ponte Reale, 2, 16124, Genoa, Italy.
| | - Mario Esposito
- Camelot Biomedical Systems S.r.l, Via Al Ponte Reale, 2, 16124, Genoa, Italy
| | - Alice Finotello
- Department of Integrated Surgical and Diagnostic Sciences, University of Genoa, Genoa, Italy
| | - Ferdinando Auricchio
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Bianca Pane
- Vascular and Endovascular Surgery Unit, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - Curzio Basso
- Camelot Biomedical Systems S.r.l, Via Al Ponte Reale, 2, 16124, Genoa, Italy
| | - Giovanni Spinella
- Vascular and Endovascular Surgery Unit, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - Michele Conti
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
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Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network. Nat Commun 2020; 11:4829. [PMID: 32973154 PMCID: PMC7518426 DOI: 10.1038/s41467-020-18606-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 08/30/2020] [Indexed: 11/24/2022] Open
Abstract
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care. Manual postprocessing of computed tomography angiography (CTA) images is extremely labor intensive and error prone. Here, the authors propose an artificial intelligence reconstruction system that can automatically achieve CTA reconstruction in healthcare services.
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