mlsanityData to 14 Oct 2026

GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation

arXiv 2406.19485 ↗First posted 27 Jun 2024eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls. It is one of the major causes of cardiovascular disease. Magnetic Resonance (MR) black-blood vessel wall imaging (BB-VWI) offers crucial insights into vascular disease diagnosis by clearly visualizing vascular structures. However, the complex anatomy of the neck poses challenges in distinguishing the carotid artery (CA) from surrounding structures, especially with changes like atherosclerosis. In order to address these issues, we propose GAPNet, which is a consisting of a novel geometric prior deduced from.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

8 of 21 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  4. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  5. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  6. 2021Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priorscited by 11
  7. 2018Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropycited by 9
  8. 2017Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networkscited by 2

Built on by

Not cited yet.