# Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation

## TL;DR

This paper demonstrates that combining residual networks trained adversarially with boundary weighted loss significantly enhances the accuracy of ischemic stroke lesion segmentation in medical images.

## Contribution

The study introduces a novel approach integrating residual learning and boundary weighted loss for improved stroke lesion segmentation.

## Key findings

- Achieved an average Dice coefficient of 0.881 for penumbra.
- Achieved an average Dice coefficient of 0.877 for core.
- Residual connections and boundary weighted loss significantly improve segmentation performance.

## Abstract

Radiologists use various imaging modalities to aid in different tasks like diagnosis of disease, lesion visualization, surgical planning and prognostic evaluation. Most of these tasks rely on the the accurate delineation of the anatomical morphology of the organ, lesion or tumor. Deep learning frameworks can be designed to facilitate automated delineation of the region of interest in such cases with high accuracy. Performance of such automated frameworks for medical image segmentation can be improved with efficient integration of information from multiple modalities aided by suitable learning strategies. In this direction, we show the effectiveness of residual network trained adversarially in addition to a boundary weighted loss. The proposed methodology is experimentally verified on the SPES-ISLES 2015 dataset for ischaemic stroke segmentation with an average Dice coefficient of $0.881$ for penumbra and $0.877$ for core. It was observed that addition of residual connections and boundary weighted loss improved the performance significantly.