Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net

TL;DR

This paper introduces a deformable U-Net model for improved segmentation and classification of sickle cell disease images, addressing challenges from cell variability and image noise.

Contribution

It proposes a novel deformable convolution integrated into U-Net, enhancing robustness to cell shape variations and image artifacts.

Findings

Deformable U-Net outperforms standard U-Net in accuracy.

The method is effective on sickle cell microscopic images.

Robust to cell shape and image quality variations.

Abstract

Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the deep U-Net structure with deformable convolution layers. The U-Net architecture for deep learning has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution layer enables the free form deformation of the feature learning process, thus makes the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for segmentation and classification, comparing with original U-Net structure.