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一种使用ResUNet对CT图像中的肝脏和肿瘤进行分割的深度学习方法。

A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet.

作者信息

Rahman Hameedur, Bukht Tanvir Fatima Naik, Imran Azhar, Tariq Junaid, Tu Shanshan, Alzahrani Abdulkareeem

机构信息

Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan.

Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China.

出版信息

Bioengineering (Basel). 2022 Aug 5;9(8):368. doi: 10.3390/bioengineering9080368.

DOI:10.3390/bioengineering9080368
PMID:36004893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9404984/
Abstract

According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.

摘要

根据《2020年全球癌症统计数据》的最新估计，肝癌是女性中第九大常见癌症。肝脏分割难度较大，而从肝脏中分割出肿瘤则更具挑战性。在获取肝脏组织样本后，会使用磁共振成像（MRI）、计算机断层扫描（CT）和超声（US）等成像测试来分割肝脏和肝肿瘤。由于软组织的强度重叠以及位置和形状的变化，基于灰度或形状从腹部计算机断层扫描图像中分割肝脏和肿瘤并不理想。本研究提出了一种使用混合ResUNet模型从CT图像体积中分割肝脏和肿瘤的更有效方法，该模型结合了ResNet和UNet模型来弥补这一差距。本研究主要使用这两个重叠模型来分割肝脏并进行感兴趣区域（ROI）评估。通过腹部CT图像体积对肝脏进行分割，以检查肝脏情况。所提出的模型基于肝肿瘤患者的CT体积切片，并在公共3D数据集IRCADB01上进行评估。基于实验分析，发现肝脏分割的真值准确率分别约为99.55%、97.85%和98.16%。骰子系数的验证率也有所提高，表明实验进展顺利，该模型已可用于肝肿瘤检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e157/9404984/efe70b828f47/bioengineering-09-00368-g020.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e157/9404984/fb7e4f8e2d20/bioengineering-09-00368-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e157/9404984/fa84038938ee/bioengineering-09-00368-g017.jpg
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