Semantic Segmentation Solution for SCD Diagnosis

Red blood cell (RBC) segmentation and classification from biomedical images is a crucial step for the diagnosis of sickle cell disease (SCD). We are developing and deploying a semantic segmentation framework to simultaneously detect and classify RBCs from raw microscopic images. The deformable U-Net (dU-net) in our proposed solution utilizes an extra deformable layer to make the prediction model more robust towards variations in the size, shape and viewpoint of the cells. Testing on preliminary data consisting of 314 images from 5 different SCD patients shows that dU-net framework achieves best segmentation/classification accuracy within an integrated workflow, outperforming both traditional unsupervised methods and classical U-Net structure.

Please see images below.

Top image : Architecture of the dU-Net in this work.

Bottom image: Binary segmentation results by different methods: (a) raw image, (b) Ilastik, (c) region growing, (d) U-Net, (e) dU-Net, (f) ground truth.

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