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Chen J, Qian L, Ma L, Urakov T, Gu W, Liang L. SymTC: A symbiotic Transformer-CNN net for instance segmentation of lumbar spine MRI. Comput Biol Med 2024; 179:108795. [PMID: 38955128 DOI: 10.1016/j.compbiomed.2024.108795] [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: 02/10/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (discs and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we proposed SymTC, an innovative lumbar spine MR image segmentation model that combines the strengths of Transformer and Convolutional Neural Network (CNN). Specifically, we designed a parallel dual-path architecture to merge CNN layers and Transformer layers, and we integrated a novel position embedding into the self-attention module of Transformer, enhancing the utilization of positional information for more accurate segmentation. To further improve model performance, we introduced a new data synthesis technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. We evaluated our SymTC and the other 16 representative image segmentation models on our private in-house dataset and public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and the 95th percentile Hausdorff Distance. The results indicate that SymTC surpasses the other 16 methods, achieving the highest dice score of 96.169 % for segmenting vertebral bones and intervertebral discs on the SSMSpine dataset. The SymTC code and SSMSpine dataset are publicly available at https://github.com/jiasongchen/SymTC.
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Affiliation(s)
- Jiasong Chen
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Linchen Qian
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Linhai Ma
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Timur Urakov
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Weiyong Gu
- Department of Mechanical and Aerospace Engineering, University of Miami, Coral Gables, FL, USA
| | - Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL, USA.
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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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Mayer C, Pepe A, Hossain S, Karner B, Arnreiter M, Kleesiek J, Schmid J, Janisch M, Hannes D, Fuchsjäger M, Zimpfer D, Egger J, Mächler H. Type B Aortic Dissection CTA Collection with True and False Lumen Expert Annotations for the Development of AI-based Algorithms. Sci Data 2024; 11:596. [PMID: 38844767 PMCID: PMC11156948 DOI: 10.1038/s41597-024-03284-2] [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: 05/15/2023] [Accepted: 04/22/2024] [Indexed: 06/09/2024] Open
Abstract
Aortic dissections (ADs) are serious conditions of the main artery of the human body, where a tear in the inner layer of the aortic wall leads to the formation of a new blood flow channel, named false lumen. ADs affecting the aorta distally to the left subclavian artery are classified as a Stanford type B aortic dissection (type B AD). This is linked to substantial morbidity and mortality, however, the course of the disease for the individual case is often unpredictable. Computed tomography angiography (CTA) is the gold standard for the diagnosis of type B AD. To advance the tools available for the analysis of CTA scans, we provide a CTA collection of 40 type B AD cases from clinical routine with corresponding expert segmentations of the true and false lumina. Segmented CTA scans might aid clinicians in decision making, especially if it is possible to fully automate the process. Therefore, the data collection is meant to be used to develop, train and test algorithms.
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Affiliation(s)
- Christian Mayer
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Antonio Pepe
- Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria
| | - Sophie Hossain
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Barbara Karner
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Melanie Arnreiter
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine (IKIM), AI-guided Therapies (AIT), Essen University Hospital (AöR), Girardetstraße 2, 45131, Essen, Germany
| | - Johannes Schmid
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Michael Janisch
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Deutschmann Hannes
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Michael Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Daniel Zimpfer
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Jan Egger
- Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria.
- Institute for Artificial Intelligence in Medicine (IKIM), AI-guided Therapies (AIT), Essen University Hospital (AöR), Girardetstraße 2, 45131, Essen, Germany.
| | - Heinrich Mächler
- Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.
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Li J, Ellis DG, Pepe A, Gsaxner C, Aizenberg MR, Kleesiek J, Egger J. Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model. J Med Syst 2024; 48:55. [PMID: 38780820 PMCID: PMC11116219 DOI: 10.1007/s10916-024-02066-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: 07/28/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024]
Abstract
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
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Affiliation(s)
- Jianning Li
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
| | - David G Ellis
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Michele R Aizenberg
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany
| | - Jan Egger
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
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Bramlet M, Mohamadi S, Srinivas J, Dassanayaka T, Okammor T, Shadden M, Sutton BP. Automating aortic cross-sectional measurement of 3D aorta models. J Med Imaging (Bellingham) 2024; 11:034503. [PMID: 38817710 PMCID: PMC11135202 DOI: 10.1117/1.jmi.11.3.034503] [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: 09/01/2023] [Revised: 04/11/2024] [Accepted: 05/03/2024] [Indexed: 06/01/2024] Open
Abstract
Purpose Aortic dissection carries a mortality as high as 50%, but surgical palliation is also fraught with morbidity risks of stroke or paralysis. As such, a significant focus of medical decision making is on longitudinal aortic diameters. We hypothesize that three-dimensional (3D) modeling affords a more efficient methodology toward automated longitudinal aortic measurement. The first step is to automate the measurement of manually segmented 3D models of the aorta. We developed and validated an algorithm to analyze a 3D segmented aorta and output the maximum dimension of minimum cross-sectional areas in a stepwise progression from the diaphragm to the aortic root. Accordingly, the goal is to assess the diagnostic validity of the 3D modeling measurement as a substitute for existing 2D measurements. Approach From January 2021 to June 2022, 66 3D non-contrast steady-state free precession magnetic resonance images of aortic pathology with clinical aortic measurements were identified; 3D aorta models were manually segmented. A novel mathematical algorithm was applied to each model to generate maximal aortic diameters from the diaphragm to the root, which were then correlated to clinical measurements. Results With a 76% success rate, we analyzed the resulting 50 3D aortic models utilizing the automated measurement tool. There was an excellent correlation between the automated measurement and the clinical measurement. The intra-class correlation coefficient and p -value for each of the nine measured locations of the aorta were as follows: sinus of valsalva, 0.99, < 0.001 ; sino-tubular junction, 0.89, < 0.001 ; ascending aorta, 0.97, < 0.001 ; brachiocephalic artery, 0.96, < 0.001 ; transverse segment 1, 0.89, < 0.001 ; transverse segment 2, 0.93, < 0.001 ; isthmus region, 0.92, < 0.001 ; descending aorta, 0.96, < 0.001 ; and aorta at diaphragm, 0.3, < 0.001 . Conclusions Automating diagnostic measurements that appease clinical confidence is a critical first step in a fully automated process. This tool demonstrates excellent correlation between measurements derived from manually segmented 3D models and the clinical measurements, laying the foundation for transitioning analytic methodologies from 2D to 3D.
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Affiliation(s)
- Matthew Bramlet
- University of Illinois College of Medicine at Peoria, Pediatric Cardiology, Peoria, Illinois, United States
| | - Salman Mohamadi
- University of Illinois Urbana Champaign, Bioengineering, Champaign, Illinois, United States
| | - Jayishnu Srinivas
- University of Illinois College of Medicine Peoria, Peoria, Illinois, United States
| | - Tehan Dassanayaka
- University of Illinois Urbana Champaign, Bioengineering, Champaign, Illinois, United States
| | - Tafara Okammor
- University of Illinois Urbana Champaign, Bioengineering, Champaign, Illinois, United States
| | | | - Bradley P. Sutton
- University of Illinois Urbana Champaign, Bioengineering, Champaign, Illinois, United States
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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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Liu H, Zhao G, Zhang GE, Xiong F, Hu S, Ouyang Y, Xiong F. Three-dimensional modelling and hemodynamic simulation of the closure of multiple entry tears in type B aortic dissection. Med Phys 2024; 51:42-53. [PMID: 38038366 DOI: 10.1002/mp.16852] [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: 12/13/2022] [Revised: 10/21/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Stanford type B aortic dissection (TB-AD) is a life-threatening vascular condition with high rates of morbidity and mortality. Currently, thoracic endovascular aortic repair (TEVAR) is widely performed to treat TB-AD, and some studies have analyzed the influence of stents on hemodynamics using computational fluid dynamics (CFD) models. However, the accuracy of TB-AD simulation models are not satisfactory, they are often constructed as a regular ideal model. Furthermore, it is unclear which tear should be closed for the best treatment when there are multi entry tears. PURPOSE The aims of this paper were to provide an assessment method for the selection of the surgical closure location for type B aortic dissection. Five 3D models of multiple entry tears in type B aortic dissection were produced using real patient computed tomography (CT) images to perform hemodynamic analyses of flow velocity streamlines, wall pressure, and wall shear stress. METHODS A Boolean operation was adopted to establish 3D models with multiple entry tears in type B aortic dissection based on patient-specific CT images. The Mimics and Ansys plug-in The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics (ICEM CFD) software were applied to mesh the 3D models. The flow velocity streamlines, wall pressures, and wall shear stresses were then analyzed in the finite element analysis software Fluent. Five 3D models were produced to compare the hemodynamic characteristics of different entry tear numbers, as well as the changes of different closure positions before and after closure. RESULTS The false lumen of the model with two entry tears had a higher wall pressure than that of model with multiple entry tears, which may tend to squeeze the true lumen and expand the false lumen. The load distribution of the vessel in the model with multiple entry tears had a more balanced flow velocity, and its wall pressure and shear stress were lower than that of model with two entry tears. For aortic dissection with two entry tears, the closure of the proximal entry tear was recommended, which helped to isolate and thrombose the false lumen, thereby improving the blood supply function of the true lumen. Because the postoperative vascular flow velocity and mechanical load performance of the vascular wall were still higher than those of normal blood vessels, the postoperative blood vessels remained pathological, and TEVAR did not restore the blood vessels to their original healthy state. CONCLUSIONS Type B aortic dissection with two entry tears tend to squeeze the true lumen and expand the false lumen, resulting in a new entry tear and deterioration into multiple entry type B aortic dissection. The model of the vessel with multiple entry tears had a more balanced distribution in flow velocity and a smaller wall pressure and shear stress than that of the vessel with two entry tears. The closure of the proximal entry tear was considered an ideal solution for type B aortic dissection with two entry tears.
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Affiliation(s)
- Hui Liu
- School of Mechanical Engineering, Guangxi University, Nanning, China
| | - Guolin Zhao
- School of Mechanical Engineering, Guangxi University, Nanning, China
| | | | - Feixiang Xiong
- School of Mechanical Engineering, Guangxi University, Nanning, China
| | - Shanshan Hu
- School of Mechanical Engineering, Guangxi University, Nanning, China
| | - Yang Ouyang
- Department of Vascular Surgery, XiangYa hospital, Central South University, Changsha, Hunan, China
| | - Fali Xiong
- School of Mechanical Engineering, Guangxi University, Nanning, China
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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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Strack C, Pomykala KL, Schlemmer HP, Egger J, Kleesiek J. "A net for everyone": fully personalized and unsupervised neural networks trained with longitudinal data from a single patient. BMC Med Imaging 2023; 23:174. [PMID: 37907876 PMCID: PMC10619304 DOI: 10.1186/s12880-023-01128-w] [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: 11/09/2022] [Accepted: 10/16/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND With the rise in importance of personalized medicine and deep learning, we combine the two to create personalized neural networks. The aim of the study is to show a proof of concept that data from just one patient can be used to train deep neural networks to detect tumor progression in longitudinal datasets. METHODS Two datasets with 64 scans from 32 patients with glioblastoma multiforme (GBM) were evaluated in this study. The contrast-enhanced T1w sequences of brain magnetic resonance imaging (MRI) images were used. We trained a neural network for each patient using just two scans from different timepoints to map the difference between the images. The change in tumor volume can be calculated with this map. The neural networks were a form of a Wasserstein-GAN (generative adversarial network), an unsupervised learning architecture. The combination of data augmentation and the network architecture allowed us to skip the co-registration of the images. Furthermore, no additional training data, pre-training of the networks or any (manual) annotations are necessary. RESULTS The model achieved an AUC-score of 0.87 for tumor change. We also introduced a modified RANO criteria, for which an accuracy of 66% can be achieved. CONCLUSIONS We show a novel approach to deep learning in using data from just one patient to train deep neural networks to monitor tumor change. Using two different datasets to evaluate the results shows the potential to generalize the method.
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Affiliation(s)
- Christian Strack
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Girardetstraße 2, 45131, Essen, Germany.
- Division of Radiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
- Medical Faculty Heidelberg, Heidelberg University, 69120, Heidelberg, Germany.
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Girardetstraße 2, 45131, Essen, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Girardetstraße 2, 45131, Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Girardetstraße 2, 45131, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147, Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147, Essen, Germany
- Department of Physics, TU Dortmund University, Otto-Hahn-Straße 4, D-44227, Dortmund, Germany
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Black SM, Maclean C, Barrientos PH, Ritos K, Kazakidi A. Reconstruction and Validation of Arterial Geometries for Computational Fluid Dynamics Using Multiple Temporal Frames of 4D Flow-MRI Magnitude Images. Cardiovasc Eng Technol 2023; 14:655-676. [PMID: 37653353 PMCID: PMC10602980 DOI: 10.1007/s13239-023-00679-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/08/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data. METHODS For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier-Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries. RESULTS Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar. CONCLUSION This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast.
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Affiliation(s)
| | - Craig Maclean
- Research and Development, Terumo Aortic, Glasgow, UK
| | - Pauline Hall Barrientos
- Clinical Physics, Queen Elizabeth University Hospital, NHS Greater Glasgow & Clyde, Glasgow, UK
| | - Konstantinos Ritos
- Department of Mechanical and Aerospace Engineering, Glasgow, UK
- Department of Mechanical Engineering, University of Thessaly, Volos, Greece
| | - Asimina Kazakidi
- Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK.
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11
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Sukumaran V, Mutlu O, Murtaza M, Alhalbouni R, Dubansky B, Yalcin HC. Experimental assessment of cardiovascular physiology in the chick embryo. Dev Dyn 2023; 252:1247-1268. [PMID: 37002896 DOI: 10.1002/dvdy.589] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 12/13/2022] [Accepted: 03/10/2023] [Indexed: 10/04/2023] Open
Abstract
High resolution assessment of cardiac functional parameters is crucial in translational animal research. The chick embryo is a historically well-used in vivo model for cardiovascular research due to its many practical advantages, and the conserved form and function of the chick and human cardiogenesis programs. This review aims to provide an overview of several different technical approaches for chick embryo cardiac assessment. Doppler echocardiography, optical coherence tomography, micromagnetic resonance imaging, microparticle image velocimetry, real-time pressure monitoring, and associated issues with the techniques will be discussed. Alongside this discussion, we also highlight recent advances in cardiac function measurements in chick embryos.
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Affiliation(s)
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha, Qatar
| | | | | | - Benjamin Dubansky
- Department of Biological and Agricultural Engineering, Office of Research and Economic Development, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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12
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Yang K, Chen M, Wang Y, Jiang G, Hou N, Wang L, Wen K, Li W. Development of a predictive risk stratification tool to identify the population over age 45 at risk for new-onset stroke within 7 years. Front Aging Neurosci 2023; 15:1101867. [PMID: 37388187 PMCID: PMC10301757 DOI: 10.3389/fnagi.2023.1101867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 05/09/2023] [Indexed: 07/01/2023] Open
Abstract
Background and purpose With the acceleration of the aging process of society, stroke has become a major health problem in the middle-aged and elderly population. A number of new stroke risk factors have been recently found. It is necessary to develop a predictive risk stratification tool using multidimensional risk factors to identify people at high risk for stroke. Methods The study included 5,844 people (age ≥ 45 years) who participated in the China Health and Retirement Longitudinal Study in 2011 and its follow-up up to 2018. The population samples were divided into training set and validation set according to 1:1. A LASSO Cox screening was performed to identify the predictors of new-onset stroke. A nomogram was developed, and the population was stratified according to the score calculated through the X-tile program. Internal and external verifications of the nomogram were performed by ROC and calibration curves, and the Kaplan-Meier method was applied to identify the performance of the risk stratification system. Results The LASSO Cox regression screened out 13 candidate predictors from 50 risk factors. Finally, nine predictors, including low physical performance and the triglyceride-glucose index, were included in the nomogram. The nomogram's overall performance was good in both internal and external validations (AUCs at 3-, 5-, and 7-year periods were 0.71, 0.71, and 0.71 in the training set and 0.67, 0.65, and 0.66 in the validation set, respectively). The nomogram was proven to excellently discriminate between the low-, moderate-, and high-risk groups, with a prevalence of 7-year new-onset stroke of 3.36, 8.32, and 20.13%, respectively (P < 0.001). Conclusion This research developed a clinical predictive risk stratification tool that can effectively identify the different risks of new-onset stroke in 7 years in the middle-aged and elderly Chinese population.
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Affiliation(s)
- Kang Yang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Minfang Chen
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaoling Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gege Jiang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Niuniu Hou
- Department of Thyroid, Breast, and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liping Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wen
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Wei Li
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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13
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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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14
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Jafarinia A, Melito GM, Müller TS, Rolf-Pissarczyk M, Holzapfel GA, Brenn G, Ellermann K, Hochrainer T. Morphological parameters affecting false lumen thrombosis following type B aortic dissection: a systematic study based on simulations of idealized models. Biomech Model Mechanobiol 2023; 22:885-904. [PMID: 36630014 PMCID: PMC10167197 DOI: 10.1007/s10237-023-01687-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 01/02/2023] [Indexed: 01/12/2023]
Abstract
Type B aortic dissection (TBAD) carries a high risk of complications, particularly with a partially thrombosed or patent false lumen (FL). Therefore, uncovering the risk factors leading to FL thrombosis is crucial to identify high-risk patients. Although studies have shown that morphological parameters of the dissected aorta are related to FL thrombosis, often conflicting results have been reported. We show that recent models of thrombus evolution in combination with sensitivity analysis methods can provide valuable insights into how combinations of morphological parameters affect the prospect of FL thrombosis. Based on clinical data, an idealized geometry of a TBAD is generated and parameterized. After implementing the thrombus model in computational fluid dynamics simulations, a global sensitivity analysis for selected morphological parameters is performed. We then introduce dimensionless morphological parameters to scale the results to individual patients. The sensitivity analysis demonstrates that the most sensitive parameters influencing FL thrombosis are the FL diameter and the size and location of intimal tears. A higher risk of partial thrombosis is observed when the FL diameter is larger than the true lumen diameter. Reducing the ratio of the distal to proximal tear size increases the risk of FL patency. In summary, these parameters play a dominant role in classifying morphologies into patent, partially thrombosed, and fully thrombosed FL. In this study, we point out the predictive role of morphological parameters for FL thrombosis in TBAD and show that the results are in good agreement with available clinical studies.
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Affiliation(s)
- Alireza Jafarinia
- Institute of Strength of Materials, Graz University of Technology, Graz, Austria.
| | - Gian Marco Melito
- Institute of Mechanics, Graz University of Technology, Graz, Austria.
| | - Thomas Stephan Müller
- Institute of Fluid Mechanics and Heat Transfer, Graz University of Technology, Graz, Austria
| | | | - Gerhard A Holzapfel
- Institute of Biomechanics, Graz University of Technology, Graz, Austria.,Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Günter Brenn
- Institute of Fluid Mechanics and Heat Transfer, Graz University of Technology, Graz, Austria
| | - Katrin Ellermann
- Institute of Mechanics, Graz University of Technology, Graz, Austria
| | - Thomas Hochrainer
- Institute of Strength of Materials, Graz University of Technology, Graz, Austria
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15
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Sendra-Balcells C, Campello VM, Martín-Isla C, Viladés D, Descalzo ML, Guala A, Rodríguez-Palomares JF, Lekadir K. Domain generalization in deep learning for contrast-enhanced imaging. Comput Biol Med 2022; 149:106052. [DOI: 10.1016/j.compbiomed.2022.106052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/09/2022] [Accepted: 08/20/2022] [Indexed: 11/03/2022]
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16
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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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17
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Qiao Y, Luo K, Fan J. Component quantification of aortic blood flow energy loss using computational fluid-structure interaction hemodynamics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106826. [PMID: 35526507 DOI: 10.1016/j.cmpb.2022.106826] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/13/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES The aorta serves as the main tube of the human blood circulation system. Energy loss (EL) occurs when blood flows through the aorta and there may be a potential correlation between EL and aortic diseases. However, the components of blood flow EL are still not fully understood. This study aims to quantitatively reveal the EL components in healthy and diseased aortas. METHODS We construct an idealized healthy aorta and three idealized representative diseased aortas: aortic aneurysm, coarctation of the aorta, and aortic dissection. Computational hemodynamic studies are carried out by using the fluid-structure interaction simulation framework. RESULTS Four kinds of EL components: viscous friction, turbulence dissipation, wall deformation, and local lesion are firstly acquired in healthy and diseased aortas based on the high-resolution blood flow information. Viscous friction contributes most to the EL (45.69%-57.22%). EL caused by the deformation of the aortic wall ranks second (15.18%-33.12%). The proportions of turbulence dissipation and local lesion depend on individual geometric characteristics. Besides, the buffering efficiency of the healthy and diseased aorta is about 80%. CONCLUSIONS This study quantitatively reports the components of blood flow EL in healthy and diseased aortas, the finding may provide novel insights into the pathogenesis of aortic diseases.
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Affiliation(s)
- Yonghui Qiao
- State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
| | - Kun Luo
- State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China; Shanghai Institute for Advanced Study of Zhejiang University, Shanghai, China.
| | - Jianren Fan
- State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China; Shanghai Institute for Advanced Study of Zhejiang University, Shanghai, China
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18
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Egger J, Gsaxner C, Pepe A, Pomykala KL, Jonske F, Kurz M, Li J, Kleesiek J. Medical deep learning-A systematic meta-review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106874. [PMID: 35588660 DOI: 10.1016/j.cmpb.2022.106874] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/22/2022] [Accepted: 05/10/2022] [Indexed: 05/22/2023]
Abstract
Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research efforts. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys.
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Affiliation(s)
- Jan Egger
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Department of Oral &Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5/1, 8036 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany.
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Department of Oral &Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5/1, 8036 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Frederic Jonske
- Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Manuel Kurz
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Jianning Li
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany
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19
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Radl L, Jin Y, Pepe A, Li J, Gsaxner C, Zhao FH, Egger J. AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks. Data Brief 2022; 40:107801. [PMID: 35059483 PMCID: PMC8760499 DOI: 10.1016/j.dib.2022.107801] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/21/2021] [Accepted: 01/04/2022] [Indexed: 11/24/2022] Open
Abstract
In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.
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Affiliation(s)
- Lukas Radl
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
| | - Yuan Jin
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Research Center for Connected Healthcare Big Data, ZhejiangLab, Hangzhou, Zhejiang, 311121 China
| | - Antonio Pepe
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
| | - Jianning Li
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Institute for AI in Medicine (IKIM), University Hospital Essen (UKE), Ruhrgebiet, Essen, Germany
| | - Christina Gsaxner
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Fen-hua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100 China
| | - Jan Egger
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Institute for AI in Medicine (IKIM), University Hospital Essen (UKE), Ruhrgebiet, Essen, Germany
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20
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Egger J, Wild D, Weber M, Bedoya CAR, Karner F, Prutsch A, Schmied M, Dionysio C, Krobath D, Jin Y, Gsaxner C, Li J, Pepe A. Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform. J Digit Imaging 2022; 35:340-355. [PMID: 35064372 PMCID: PMC8782222 DOI: 10.1007/s10278-021-00574-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 02/06/2023] Open
Abstract
Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster (www.studierfenster.at): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448–456, 68], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology.
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Affiliation(s)
- Jan Egger
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia.
- Computer Algorithms for Medicine Laboratory, Graz, Austria.
- Institute for Artificial Intelligence in Medicine, AI-guided Therapies, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
| | - Daniel Wild
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Maximilian Weber
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christopher A Ramirez Bedoya
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Florian Karner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Alexander Prutsch
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Michael Schmied
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christina Dionysio
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Dominik Krobath
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Yuan Jin
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Research Center for Connected Healthcare Big Data, ZhejiangLab, 311121, Hangzhou, Zhejiang, China
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Jianning Li
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Institute for Artificial Intelligence in Medicine, AI-guided Therapies, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
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21
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Celi S, Vignali E, Capellini K, Gasparotti E. On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:748908. [PMID: 35047960 PMCID: PMC8757785 DOI: 10.3389/fmedt.2021.748908] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022] Open
Abstract
The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.
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Affiliation(s)
- Simona Celi
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana Gabriele Monasterio, Massa, Italy
| | - Emanuele Vignali
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana Gabriele Monasterio, Massa, Italy
| | - Katia Capellini
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana Gabriele Monasterio, Massa, Italy.,Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Emanuele Gasparotti
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana Gabriele Monasterio, Massa, Italy.,Department of Information Engineering, University of Pisa, Pisa, Italy
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22
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Spindelböck T, Ranftl S, von der Linden W. Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals. ENTROPY 2021; 23:e23121661. [PMID: 34945967 PMCID: PMC8700029 DOI: 10.3390/e23121661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 11/20/2022]
Abstract
An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9±0.1)% and a sensitivity of (97.5±0.1)%. A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.
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Affiliation(s)
- Tobias Spindelböck
- Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria; (T.S.); (W.v.d.L.)
| | - Sascha Ranftl
- Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria; (T.S.); (W.v.d.L.)
- Graz Center of Computational Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria
- Correspondence:
| | - Wolfgang von der Linden
- Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria; (T.S.); (W.v.d.L.)
- Graz Center of Computational Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria
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23
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Wang Y, Bai L, Yang J, Lu Y, Fan W, Nie Z, Yu J, Wen K, Wang R, He L, Yang F, Qi B. Artificial intelligence measuring the aortic diameter assist in identifying adverse blood pressure status including masked hypertension. Postgrad Med 2021; 134:111-121. [PMID: 34762815 DOI: 10.1080/00325481.2021.2003150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION AND OBJECTIVES Artificial intelligence (AI) made it achievable that aortic dilation could be measured in CT images indirectly, while aortic diameter (AD) has the certain relationship with blood pressure. It was potential that the blood pressure condition be determined by AD measurement using the data obtained from a CT scanning especially in identifying masked hypertension and predicting the risk of poor control of blood pressure (BP) which was easy to elude diagnosis in clinic. We aimed to evaluate the possibility of utilizing AD by AI for predicting the risk of adverse BP status (including masked hypertension or poor BP control) and the optimal thoracic aortic position in measurement as well as the cutoff value for predicting the risk. METHODS Eight hundred and one patients were enrolled in our study. AI-Rad Companion Cardiovascular (K183268 FDA approved) was used to perform automatic aorta measurement in thoracic CT images at nine key positions based on AHA guidelines. Data was post processed by software from AI-Rad Companion undergone rigorous clinical validation by both FDA and CE as verification of its efficacy and usability. The AD's risk and diagnostic value was assessed in identifying hypertension in the general population, in identifying the poor BP controlled in the hypertension population, and in screening masked hypertension in the general population respectively by multiple regression analysis and receiver operating curve analysis. RESULTS AD measured by AI was a risk factor for adverse BP status after clinical covariates adjustment (OR = 1.02 ~ 1.26). The AD at mid descending aorta was mostly affected by BP particularly, which is optimal indicator in identifying hypertension in the general population (AUC = 0.73) and for screening masked hypertension (AUC = 0.78). CONCLUSION Using AI to measure the AD of the aorta, particularly at the position of mid descending aorta, is greatly valuable for identifying people with poor BP status. It will be possible to reveal more clinical information reflected by ordinary CT images and enrich the screening methods for hypertension, especially masked hypertension.PLAIN LANGUAGE SUMMARYHTN has a significant adverse effect on arterial deformation. BP and arterial dilation promote each other in a vicious circle. Arterial dilation may not be restricted by apparent fluctuations in BP and is objective evidence of an undesirable BP state. The accuracy of AD measurements by AI on chest CT images has been verified. There has not been the application of AD measurement by AI in the scene of poor BP status in clinical practice.In this study, we applied AI to measure the diameter of the aorta in nine consecutive positions. We explored the association between AD at various positions and BP levels and the possibility that AD in identifying poor BP status in different populations. We found that the AD at the MD is of great value in screening MH and evaluating the control state of BP in HTN. It will be possible to significantly expand the clinical information reflected by ordinary CT images and enrich the screening methods for HTN, especially MH.
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Affiliation(s)
- Yaoling Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lijuan Bai
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yichen Lu
- Digital Health, Siemens Healthineers Digital Technology(Shanghai)Co., Ltd, Shanghai, China
| | - Wenliang Fan
- Department of Software Engineering and Data Technology, School of Software & Microelectronics, Peking University, Beijing, China
| | - Zhuang Nie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Yu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wen
- Department of Software Engineering and Data Technology, School of Software & Microelectronics, Peking University, Beijing, China
| | - Ruiyun Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linfeng He
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Benling Qi
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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24
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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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25
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Egger J, Pepe A, Gsaxner C, Jin Y, Li J, Kern R. Deep learning-a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact. PeerJ Comput Sci 2021; 7:e773. [PMID: 34901429 PMCID: PMC8627237 DOI: 10.7717/peerj-cs.773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 10/15/2021] [Indexed: 05/07/2023]
Abstract
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.
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Affiliation(s)
- Jan Egger
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Department of Oral and Maxillofacial Surgery, Medical University of Graz, Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen, Essen, Germany
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Department of Oral and Maxillofacial Surgery, Medical University of Graz, Graz, Austria
| | - Yuan Jin
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Research Center for Connected Healthcare Big Data, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Jianning Li
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen, Essen, Germany
- Research Unit Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - Roman Kern
- Knowledge Discovery, Know-Center, Graz, Austria
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
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26
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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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27
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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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Yang J, Fu M, Hu Y. Liver vessel segmentation based on inter-scale V-Net. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4327-4340. [PMID: 34198439 DOI: 10.3934/mbe.2021217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.
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Affiliation(s)
- Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education Northeastern University, Shenyang 110000, China
| | - Meihan Fu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
| | - Ying Hu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
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Moran M, Faria M, Giraldi G, Bastos L, Conci A. Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution? SENSORS 2021; 21:s21062013. [PMID: 33809165 PMCID: PMC8000288 DOI: 10.3390/s21062013] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 11/25/2022]
Abstract
Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion difficult. Recently, the use of deep-learning methods for image resolution improvement has seen an increase in the literature. In this work, we performed two studies to evaluate the effects of using different resolution improvement methods (nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first one, specialized dentists visually analyzed the quality of images treated with these techniques. In the second study, we used those methods as different pre-processing steps for inputs of convolutional neural network (CNN) classifiers (Inception and ResNet) and evaluated whether this process leads to better results. The deep-learning methods lead to a substantial improvement in the visual quality of images but do not necessarily promote better classifier performance.
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Affiliation(s)
- Maira Moran
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.)
- Instituto de Computação, Universidade Federal Fluminense, Niterói 24210-310, Brazil
- Correspondence: (M.M.); (A.C.)
| | - Marcelo Faria
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.)
- Faculdade de Odontologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil
| | - Gilson Giraldi
- Laboratório Nacional de Computação Científica, Petrópolis 25651-076, Brazil;
| | - Luciana Bastos
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.)
| | - Aura Conci
- Instituto de Computação, Universidade Federal Fluminense, Niterói 24210-310, Brazil
- Correspondence: (M.M.); (A.C.)
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