Anatomy Prior Based U-net for Pathology Segmentation with Attention

• URL: http://arxiv.org/abs/2011.08769v1
• Date: Tue, 17 Nov 2020 16:52:29 GMT
• Title: Anatomy Prior Based U-net for Pathology Segmentation with Attention
• Authors: Yuncheng Zhou and Ke Zhang and Xinzhe Luo and Sihan Wang and Xiahai Zhuang
• Abstract summary: We propose an anatomy prior based framework, which combines the U-net segmentation network with the attention technique. We propose a neighborhood penalty strategy to gauge the inclusion relationship between the myocardium and the myocardial infarction and no-reflow areas. Results show that our framework is effective in pathological area segmentation.
• Score: 11.266069499113966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and small area, pathological segmentation has always been a challenging task. We propose an anatomy prior based framework, which combines the U-net segmentation network with the attention technique. Leveraging the fact that the pathology is inclusive, we propose a neighborhood penalty strategy to gauge the inclusion relationship between the myocardium and the myocardial infarction and no-reflow areas. This neighborhood penalty strategy can be applied to any two labels with inclusive relationships (such as the whole infarction and myocardium, etc.) to form a neighboring loss. The proposed framework is evaluated on the EMIDEC dataset. Results show that our framework is effective in pathological area segmentation.

Related papers

• Anatomy-guided Pathology Segmentation [56.883822515800205]
  We develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods.
  arXiv  Detail & Related papers  (2024-07-08T11:44:15Z)
• Crop and Couple: cardiac image segmentation using interlinked specialist networks [0.5452923068355806]
  We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy. Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies.
  arXiv  Detail & Related papers  (2024-02-14T13:14:04Z)
• MPSeg : Multi-Phase strategy for coronary artery Segmentation [9.767759441883008]
  We present MPSeg, an innovative multi-phase strategy designed for coronary artery segmentation. Our approach specifically accommodates these structural complexities and adheres to the principles of the SYNTAX Score. Notably, our approach demonstrated exceptional effectiveness when evaluated in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T03:33:09Z)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Structure-aware registration network for liver DCE-CT images [50.28546654316009]
  We propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Our proposed method can achieve higher registration accuracy and preserve anatomical structure more effectively than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-08T14:08:56Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images [23.212429566838203]
  We develop a novel auto-weighted supervision framework to tackle the scar and edema segmentation from multi-sequence CMR data. We also design a coarse-to-fine framework to boost the small myocardial pathology region segmentation with shape prior knowledge. Our method is promising in advancing the myocardial pathology assessment on multi-sequence CMR data.
  arXiv  Detail & Related papers  (2022-01-14T08:59:54Z)
• MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
  This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
  arXiv  Detail & Related papers  (2022-01-10T06:37:23Z)
• Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images [3.5354617056939874]
  This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN)
  arXiv  Detail & Related papers  (2020-08-13T09:57:04Z)
• Learning Directional Feature Maps for Cardiac MRI Segmentation [13.389141642517762]
  We propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes. Specifically, we perform cardiac segmentation and learn a direction field pointing away from the nearest cardiac tissue boundary to each pixel. Based on the learned direction field, we then propose a feature rectification and fusion (FRF) module to improve the original segmentation features.
  arXiv  Detail & Related papers  (2020-07-22T11:31:04Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.