# Anatomy Prior Based U-net for Pathology Segmentation with Attention

arXiv: https://arxiv.org/abs/2011.08769 · HTML: https://mlsanity.com/paper/2011.08769/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge](https://mlsanity.com/paper/2108.04016/) (2021)
- [Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images](https://mlsanity.com/paper/2306.14725/) (2023)

## Abstract (arXiv)

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.