Neural Network-based Fast Liver Ultrasound Image Segmentation

被引：16

作者：

Ansari, Mohammed Yusuf ^{[1
]}

Mangalote, Iffa Afsa Changaai ^{[1
]}

Masri, Dima ^{[2
]}

Dakua, Sarada Prasad ^{[1
]}

机构：

[1] Hamad Med Corp, Hamad Gen Hosp, Surg, Doha, Qatar

[2] King Faisal Univ, Biomed Engineeting, Al Hufuf, Al Ahsa, Saudi Arabia

来源：

2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN | 2023年

关键词：

Liver; Ultrasound; Segmentation; Fast; FUSION;

D O I：

10.1109/IJCNN54540.2023.10191085

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ultrasound is quite popular among the clinicians' fraternity, because the machine is cheap, easy to use, and mobile. However, the image is not that easy to study properly, if the examiner does not have adequate expertise. Image segmentation, being the first step to ease the study, is considered crucial. In this paper, we have presented a neural network-based image segmentation that is based on Pyramid Scene Parsing. Additionally, we have studied the importance of removing noise before the actual segmentation. We have tested the method on the data obtained from Hamad Medical Corporation and found Dice coefficient of 0.913 +/- 0.024 while delivering a real-time performance of 37 frames per second.

引用

页数：8

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