Femoral segmentation of MRI images using PP-LiteSeg

被引：1

作者：

Peng, Boyuan ^{[1
]}

Liu, Yiyang ^{[1
]}

Zhu, Xin ^{[1
]}

Ikeda, Shouhei ^{[2
]}

Tsunoda, Saburo ^{[2
]}

机构：

[1] Univ Aizu, Biomed Informat Engn Lab, Aizu Wakamatsu, Fukushima, Japan

[2] Aizu Med Ctr, Div Hematol, Aizu Wakamatsu, Fukushima, Japan

来源：

2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22) | 2022年

关键词：

Femoral; PP-LiteSeg; MRI; semantic segmentation;

D O I：

10.1109/BHI56158.2022.9926879

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：4

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