  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.
