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Postiglione TJ, Guillo E, Heraud A, Rossillon A, Bartoli M, Herpe G, Adam C, Fabre D, Ardon R, Azarine A, Haulon S. Multicentric clinical evaluation of a computed tomography-based fully automated deep neural network for aortic maximum diameter and volumetric measurements. J Vasc Surg 2024; 79:1390-1400.e8. [PMID: 38325564 DOI: 10.1016/j.jvs.2024.01.214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVE This study aims to evaluate a fully automatic deep learning-based method (augmented radiology for vascular aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements. METHODS A clinical validation dataset was constructed from preoperative and postoperative aortic computed tomography angiography (CTA) scans for assessing these functions. The dataset totaled 350 computed tomography angiography scans from 216 patients treated at two different hospitals. ARVA's ability to segment the aorta into seven morphologically based aortic segments and measure maximum outer-to-outer wall transverse diameters and compute volumes for each was compared with the measurements of six experts (ground truth) and thirteen clinicians. RESULTS Ground truth (experts') measurements of diameters and volumes were manually performed for all aortic segments. The median absolute diameter difference between ground truth and ARVA was 1.6 mm (95% confidence interval [CI], 1.5-1.7; and 1.6 mm [95% CI, 1.6-1.7]) between ground truth and clinicians. ARVA produced measurements within the clinical acceptable range with a proportion of 85.5% (95% CI, 83.5-86.3) compared with the clinicians' 86.0% (95% CI, 83.9-86.0). The median volume similarity error ranged from 0.93 to 0.95 in the main trunk and achieved 0.88 in the iliac arteries. CONCLUSIONS This study demonstrates the reliability of a fully automated artificial intelligence-driven solution capable of quick aortic segmentation and analysis of both diameter and volume for each segment.
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Affiliation(s)
- Thomas J Postiglione
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France
| | - Enora Guillo
- Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Alexandre Heraud
- Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | | | | | - Guillaume Herpe
- DACTIM MIS Lab, I3M, CNRS UMR, Poitiers, France; Incepto Medical, Paris, France
| | | | - Dominique Fabre
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France
| | | | - Arshid Azarine
- Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Stéphan Haulon
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France.
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Liu L, Chen D, Shu M, Li B, Shu H, Paques M, Cohen LD. Trajectory Grouping With Curvature Regularization for Tubular Structure Tracking. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 31:405-418. [PMID: 34874858 DOI: 10.1109/tip.2021.3131940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.
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Adam C, Fabre D, Mougin J, Zins M, Azarine A, Ardon R, d'Assignies G, Haulon S. Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence. Eur J Vasc Endovasc Surg 2021; 62:869-877. [PMID: 34518071 DOI: 10.1016/j.ejvs.2021.07.013] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/04/2021] [Accepted: 07/11/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate an automatic, deep learning based method (Augmented Radiology for Vascular Aneurysm [ARVA]), to detect and assess maximum aortic diameter, providing cross sectional outer to outer aortic wall measurements. METHODS Accurate external aortic wall diameter measurement is performed along the entire aorta, from the ascending aorta to the iliac bifurcations, on both pre- and post-operative contrast enhanced computed tomography angiography (CTA) scans. A training database of 489 CTAs was used to train a pipeline of neural networks for automatic external aortic wall measurements. Another database of 62 CTAs, including controls, aneurysmal aortas, and aortic dissections scanned before and/or after endovascular or open repair, was used for validation. The measurements of maximum external aortic wall diameter made by ARVA were compared with those of seven clinicians on this validation dataset. RESULTS The median absolute difference with respect to expert's measurements ranged from 1 mm to 2 mm among all annotators, while ARVA reported a median absolute difference of 1.2 mm. CONCLUSION The performance of the automatic maximum aortic diameter method falls within the interannotator variability, making it a potentially reliable solution for assisting clinical practice.
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Affiliation(s)
| | - Dominique Fabre
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France
| | - Justine Mougin
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France
| | - Marc Zins
- Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Arshid Azarine
- Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | | | | | - Stephan Haulon
- Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France.
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Mou L, Zhao Y, Fu H, Liu Y, Cheng J, Zheng Y, Su P, Yang J, Chen L, Frangi AF, Akiba M, Liu J. CS 2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Med Image Anal 2020; 67:101874. [PMID: 33166771 DOI: 10.1016/j.media.2020.101874] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/26/2020] [Accepted: 10/05/2020] [Indexed: 12/20/2022]
Abstract
Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics.
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Affiliation(s)
- Lei Mou
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
| | - Huazhu Fu
- Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Yonghuai Liu
- Department of Computer Science, Edge Hill University, Ormskirk, UK
| | - Jun Cheng
- UBTech Research, UBTech Robotics Corp Ltd, Shenzhen, China
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, UK; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Pan Su
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jianlong Yang
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Li Chen
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Alejandro F Frangi
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK; Medical Imaging Research Centre (MIRC), University Hospital Gasthuisberg, Cardiovascular Sciences and Electrical Engineering Departments, KU Leuven, Leuven, Belgium
| | | | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
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Cheng Z, Lu X, Feng B. A review of research progress of antitumor drugs based on tubulin targets. Transl Cancer Res 2020; 9:4020-4027. [PMID: 35117769 PMCID: PMC8797889 DOI: 10.21037/tcr-20-682] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 04/30/2020] [Indexed: 12/18/2022]
Abstract
Microtubules exist in all eukaryotic cells and are one of the critical components that make up the cytoskeleton. Microtubules play a crucial role in supporting cell morphology, cell division, and material transport. Tubulin modulators can promote microtubule polymerization or cause microtubule depolymerization. The modulators interfere with the mitosis of cells and inhibit cell proliferation. Tubulin mainly has three binding domains, namely, paclitaxel, vinca and colchicine binding domains, which are the best targets for the development of anticancer drugs. Currently, drugs for tumor therapy have been developed for these three domains. However, due to its narrow therapeutic window, poor selectivity, and susceptibility to drug resistance, it has severely limited clinical applications. The method of combined medication, the change of administration method, the modification of compound structure, and the research and development of new targets have all changed the side effects of tubulin drugs to a certain extent. In this review, we briefly introduce a basic overview of tubulin and the main mechanism of anti-tumor. Secondly, we focus on the application of drugs which developed based on the three domains of tubulin to various cancers in various fields. Finally, we further provide the development progress of tubulin inhibitors currently in clinical trials.
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Affiliation(s)
- Ziqi Cheng
- College of Life Science and Technology, Dalian University, Dalian, China
| | - Xuan Lu
- College of Life Science and Technology, Dalian University, Dalian, China
| | - Baomin Feng
- College of Life Science and Technology, Dalian University, Dalian, China
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Hao D, Ding S, Qiu L, Lv Y, Fei B, Zhu Y, Qin B. Sequential vessel segmentation via deep channel attention network. Neural Netw 2020; 128:172-187. [PMID: 32447262 DOI: 10.1016/j.neunet.2020.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 04/22/2020] [Accepted: 05/04/2020] [Indexed: 02/01/2023]
Abstract
Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method's superior performance over other methods in terms of the quantitative metrics and visual validation. To facilitate the reproductive research in XCA community, we publicly release our dataset and source codes at https://github.com/Binjie-Qin/SVS-net.
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Affiliation(s)
- Dongdong Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Song Ding
- Department of Cardiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Linwei Qiu
- School of Astronautics, Beihang University, Beijing 100191, China
| | - Yisong Lv
- School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Baowei Fei
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Yueqi Zhu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University, 600 Yi Shan Road, Shanghai 200233, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Zhang J, Qiao Y, Sarabi MS, Khansari MM, Gahm JK, Kashani AH, Shi Y. 3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1335-1346. [PMID: 31647423 PMCID: PMC7174137 DOI: 10.1109/tmi.2019.2948867] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences ( ) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.
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Affiliation(s)
- Jiong Zhang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; USC Roski Eye Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - Yuchuan Qiao
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Mona Sharifi Sarabi
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Maziyar M. Khansari
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; USC Roski Eye Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - Jin K. Gahm
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Amir H. Kashani
- USC Roski Eye Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
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