分割
计算机科学
人工智能
软件可移植性
Sørensen–骰子系数
棱锥(几何)
人工神经网络
推论
掷骰子
模式识别(心理学)
图像分割
计算机视觉
机器学习
统计
物理
光学
程序设计语言
数学
作者
Mohammed Yusuf Ansari,Yin Yang,Pramod Kumar Meher,Sarada Prasad Dakua
标识
DOI:10.1016/j.compbiomed.2022.106478
摘要
A C TLiver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-ofuse, portability, and non-invasive nature.Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures.In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy.Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance.We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024while delivering a real-time performance of 37 frames per second (FPS).
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