Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography

Zhang, Hongwei; Gao, Zhifan; Zhang, Dong; Hau, William Kongto; Zhang, Heye

doi:10.1109/tmi.2022.3219126

Cited by 26 publications

(10 citation statements)

References 60 publications

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“…An alternative metric for training loss is focal Dice loss, which could alleviate the imbalance between empirically defined subtypes [52]. Models that extract semantic features could integrate spatial information to improve sensitivity to tumors at smaller sizes and tissue boundaries, making it worthwhile to validate their efficacy in the TNBC population [53,54]. Finally, a systematic comparison of our model to the conventional models using the same datasets would better evaluate our model.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Xu,

Rauch,

Mohamed

et al. 2023

Cancers

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Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients’ treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Xu,

Rauch,

Mohamed

et al. 2023

Cancers

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“…Zhang et al . introduced a progressive perception learning (PPL) framework for main coronary segmentation in X-ray angiography 13 . This approach addresses challenges faced by traditional and existing deep learning methods by employing context, interference, and boundary perception modules.…”

Section: Related Workmentioning

confidence: 99%

Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

Popov,

Amanturdieva,

Zhaksylyk

et al. 2024

Sci Data

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X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.

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“…Due to its excellent performance in semantic segmentation, deep learning has been widely applied in medical image analysis and assisted diagnosis [24]. Its applications include main coronary artery segmentation [25], COVID-19 lung lesion segmentation [26], prostate gland segmentation [27], brain tumor segmentation [28,29], and melanin skin disease segmentation [30]. As a common structure used in medical image segmentation, U-Net combines low-level feature information with high-level feature information through skip connections from encoder to decoder and has achieved better segmentation results with a limited number of data samples.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

A graph‐based edge attention gate medical image segmentation method

Hao

2023

IET Image Processing

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For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID‐19 CT scans) were used to evaluate the performances of various models; multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation.

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Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography

Cited by 26 publications

References 60 publications

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

A graph‐based edge attention gate medical image segmentation method

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