Semantic Segmentation of Intracranial Hemorrhages in Head CT Scans

被引：0

作者：

Qiu, Yuhang ^{[1
]}

Chang, Chia Shuo ^{[2
]}

Yan, Jiun Lin ^{[3
]}

Ko, Li ^{[3
]}

Chang, Tian Sheuan ^{[2
]}

机构：

[1] Fuzhou Univ, Elect Informat Engn, Fuzhou, Fujian, Peoples R China

[2] Natl Chiao Tung Univ, Inst Elect, Hsinchu, Taiwan

[3] Chang Gung Mem Hosp, Dept Neurosurg, Taoyuan, Taiwan

来源：

PROCEEDINGS OF 2019 IEEE 10TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2019) | 2019年

关键词：

component-segmentation; intracranial hemorrhage; U-Net; pretrained; blood loss; CLASSIFICATION; ALGORITHM;

D O I：

10.1109/icsess47205.2019.9040733

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a semantic segmentation method that can distinguish six different types of intracranial hemorrhage and calculate the amount of blood loss. The major challenge of medical image segmentation are the lack of enough data due to the difficulty of data collection and labeling. In this paper, we propose to adopt a pretrained U-Net model with fine tuning to solve this problem. The best final test accuracy can reach 94.1% which is 10.5% higher than the model training from scratch, proving its advantages in dealing with relatively complex datasets with a small amount of data, and the success of the proposed segmentation method.

引用

页码：112 / 115

页数：4

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