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

共 50 条

[31] 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement
Zeng, Yuhang
Zou, Yunhao
Fu, Ying
PATTERN RECOGNITION AND COMPUTER VISION,, PT III, 2021, 13021 : 66 - 77
[32] A Joint 3D UNet-Graph Neural Network-Based Method for Airway Segmentation from Chest CTs
Juarez, Antonio Garcia-Uceda
Selvan, Raghavendra
Saghir, Zaigham
de Bruijne, Marleen
MACHINE LEARNING IN MEDICAL IMAGING (MLMI 2019), 2019, 11861 : 583 - 591
[33] Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT
Wang, Jinke
Zhang, Xiangyang
Guo, Liang
Shi, Changfa
Tamura, Shinichi
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023, 20 (01) : 1297 - 1316
[34] Improving Automated Lesion Detection and Segmentation in PET/CT Scans: A Comparative Study of 3D UNet-Based Configurations
Namias, M.
Rotstein Habarnau, Y. V.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 50 (SUPPL 1) : S99 - S100
[35] An improved 3D-UNet-based brain hippocampus segmentation model based on MR images
Yang, Qian
Wang, Chengfeng
Pan, Kaicheng
Xia, Bing
Xie, Ruifei
Shi, Jiankai
BMC MEDICAL IMAGING, 2024, 24 (01):
[36] Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images
Junjie Wang
Yuanyuan Yu
Rongyao Yan
Jie Liu
Hao Wu
Daoying Geng
Zekuan Yu
International Journal of Computer Assisted Radiology and Surgery, 2021, 16 : 1727 - 1736
[37] PAT-Unet: Paired Attention Transformer for Efficient and Accurate Segmentation of 3D Medical Images
Zou, Qingzhi
Zhao, Jing
Li, Ming
Yuan, Lin
PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XIII, 2024, 14437 : 358 - 369
[38] Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images
Wang, Junjie
Yu, Yuanyuan
Yan, Rongyao
Liu, Jie
Wu, Hao
Geng, Daoying
Yu, Zekuan
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2021, 16 (10) : 1727 - 1736
[39] 3D Neuron Segmentation Based on 3D DSAC U-Net
Guilin University of Electronic Technology, School of Computer Science and Information Security, Guilin
541004, China
不详
514000, China
不详
541004, China
不详
541004, China
Proc. - Int. Conf. Digit. Home, ICDH, (322-326):
[40] Fast and Efficient Brain Extraction with Recursive MLP Based 3D UNet
Shangguan, Guoqing
Xiong, Hao
Liu, Dong
Shen, Hualei
NEURAL INFORMATION PROCESSING, ICONIP 2023, PT III, 2024, 14449 : 578 - 588

← 1 2 3 4 5 →