RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net

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 U-Net structure with deformable convolution layers. The U-Net architecture has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution enables the free form deformation of the feature learning process, thus making 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 both segmentation and classification tasks, compared with the original U-Net structure and unsupervised methods.