Rib Fracture Segmentation Based on 3D FM-Unet

被引：1

作者：

Zhao, Bin ^{[1
]}

Yang, Ting ^{[1
]}

Xin, Zhaowei ^{[2
]}

Wang, Chunshi ^{[1
]}

机构：

[1] Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin 541004, Guangxi, Peoples R China

[2] Black Sesame Technol Co Lt, Shanghai 201203, Peoples R China

来源：

PROCEEDINGS OF 2023 4TH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCE, ISAIMS 2023 | 2023年

基金：

中国国家自然科学基金;

关键词：

Computed Tomography; Rib Fracture Segmentation; 3D FM-UNet;

D O I：

10.1145/3644116.3644132

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Computed tomography (CT) scanning is a common method for diagnosing rib fractures. However, the shape of the fracture region obtained from CT image segmentation seriously affects the clinician's diagnosis. To solve this problem, we propose a segmentation network (3D FM-UNet) to segment CT images to obtain more accurate regions of rib fractures. The experiments show that our 3D FM-UNet achieves an average mean Dice score of 0.5452 and an average IoU of 0.3748, which exceeds the comparison methods.

引用

页码：80 / 83

页数：4

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