Boyuan Peng, Yiyang Liu, Xin Zhu, Shouhei Ikeda, S. Tsunoda
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Femoral segmentation of MRI images using PP-LiteSeg
Hematological malignancies are a lethal disease that seriously endangers human lives. In addition to bone marrow biopsy, the use of MRI to analyze the bone marrow of femur is a new and efficient diagnostic method for hematological tumors. Accurate segmentation of femur plays a crucial role in screening this disease. In this paper, we compared four neural networks (PP-LiteSeg, U-Net, SegNet, and PspNet) for femur segmentation using 579 training and testing MRI images from 200 patients with HM. PP-LiteSeg demonstrated the best performance with an average Dice coefficient of 0.92.
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