Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge

Gharleghi, Ramtin; Adikari, D.; Ellenberger, K.; Yu, Jennifer; Ellis, Chris; Chen, Chung-Ming; Gao, Rongli; He, Yuting; Hussain, Raabid; Lee, Chia-Yen; Li, Jun; Ma, Jun; Nie, Ziwei; Oliveira, Bruno; Qi, Yaolei; Skandarani, Youssef; Wang, Xiyue; Yang, Sen; Sowmya, Arcot; Beier, Susann

doi:10.1016/j.compmedimag.2022.102049

Cited by 36 publications

(12 citation statements)

References 16 publications

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“…Data We collected three categories of datasets described in table 1: 1) cardiac computed tomography angiography (CTA); 2) brain magnetic resonance imaging (MRI); 3) histopathology images, respectively. More specifically, we collected three public cardiac CTA datasets acquired from globally different institutes: the Whole Heart Segmentation (WHS) challenge dataset 39 , Automated Segmentation of Coronary Arteries (ASOCA) challenge 2020 dataset 40 , and MICCAI Coronary Artery Tracking (CAT08) Challenge 2008 dataset 41 . The heterogeneity of scanners and radiology protocols result in various range of voxel spacing and image quality.…”

Section: Methodsmentioning

confidence: 99%

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Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

Chang

Yan²,

Zhou³

et al. 2022

Preprint

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Overcoming barriers of multi-center data analysis is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose Distributed Synthetic Learning (DSL) architecture to learn across multi-medical centers without leaking sensitive personal information. DSL emphasizes the building of a homogeneous data center with entirely synthetic medical images via a form of GAN-based synthetic learning. In particular, DSL architecture is extensible with three key variances: multi-modality learning, missing modality completion learning, and continuous learning over time. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider for various image tasks from an ideal synthetic image quality metric called Dist-FID. We show that our model can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%. The proposed DSL framework demonstrates its potential for integrating multi-center heterogeneous data to support downstream clinical decision making.

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Section: Methodsmentioning

confidence: 99%

“…For the Cardiac CTA data, we collected three public CTA datasets acquired from globally different institutes: WHS dataset 39,43 , ASOCA challenge 2020 dataset 40 , MICCAI CAT08 Challenge dataset 41 . The WHS data have manually annotated labels of seven whole heart substructures.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

Chang

Yan²,

Zhou³

et al. 2022

Preprint

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“…47 Virtual models of the coronary anatomy will be reconstructed from the CTCA image using deep convolutional neural networks based on nnU-Net architecture, 48 as this method has been shown to work well in automated coronary artery segmentation. 49 After Taubin’s algorithm smoothing and vessel centrelines extraction with Vascular Modelling Toolkit, 50 relevant geometric arterial tree features will be quantified using in-house python scripts. This includes the median branch diameters, tortuosities, curvature (Frenet-Serret formulas with the average curvature used for analysis).…”

Section: Methods and Analysismentioning

confidence: 99%

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

et al. 2022

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IntroductionCoronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The ‘anatomy of risk’ hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD.Methods and analysisGeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual’s CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool.Ethics and disseminationThe study protocol has been approved by the St Vincent’s Hospital Human Research Ethics Committee, Sydney—2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee—2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.

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“…1) Few-shot cardiac structures segmentation [41] evaluates our framework on seven big cardiac structures on computed tomography (CT) images. Three public available datasets are introduced into our evaluation, including the MM-WHS [41], which has 20 images with cardiac structures' labels and 40 unlabeled images, ASOCA [42], which has 60 unlabeled images, and CAT08 [43], which has 32 images with cardiac structures' labels from. 1 Totally, 52 labeled and 100 unlabeled images are used in our evaluation.…”

Section: A Datasetsmentioning

confidence: 99%

Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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In this work, we address the task of few-shot medical image segmentation (MIS) with a novel proposed framework based on the learning registration to learn segmentation (LRLS) paradigm. To cope with the limitations of lack of authenticity, diversity, and robustness in the existing LRLS frameworks, we propose the better registration better segmentation (BRBS) framework with three main contributions that are experimentally shown to have substantial practical merit. First, we improve the authenticity in the registration-based generation program and propose the knowledge consistency constraint strategy that constrains the registration network to learn according to the domain knowledge. It brings the semanticaligned and topology-preserved registration, thus allowing the generation program to output new data with great space and style authenticity. Second, we deeply studied the diversity of the generation process and propose the space-style sampling program, which introduces the modeling of the transformation path of style and space change between few atlases and numerous unlabeled images into the generation program. Therefore, the sampling on Manuscript

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Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge

Cited by 36 publications

References 16 publications

Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness

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