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应用于脑部计算机断层扫描的结构分割无监督模型。

Unsupervised model for structure segmentation applied to brain computed tomography.

作者信息

Dos Santos Paulo Victor, Scoczynski Ribeiro Martins Marcella, Amorim Nogueira Solange, Gonçalves Cristhiane, Maffei Loureiro Rafael, Pacheco Calixto Wesley

机构信息

Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.

Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Sao Paulo, Brazil.

出版信息

PLoS One. 2024 Jun 13;19(6):e0304017. doi: 10.1371/journal.pone.0304017. eCollection 2024.

DOI:10.1371/journal.pone.0304017
PMID:38870119
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11175403/
Abstract

This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.

摘要

本文提出了一种用于分割脑部计算机断层扫描的无监督方法。所提出的方法涉及图像特征提取以及应用相似性和连续性约束来生成解剖头部结构的分割图。该方法专门针对真实世界数据集设计，应用了根据所需结构数量定制的空间连续性评分函数。主要目标是通过识别具有特定异常的区域来协助医学专家进行诊断。结果表明这是一种简化且易于使用的解决方案，可减少计算量、训练时间和财务成本。此外，该方法具有加快对异常扫描解释的潜力，从而对临床实践产生影响。这种提出的方法可能成为分割脑部计算机断层扫描的实用工具，并在研究和临床环境中对医学图像分析做出重大贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/8c63147477f3/pone.0304017.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/20e6d012e3e9/pone.0304017.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/69acaebbc285/pone.0304017.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/8b7497b119d7/pone.0304017.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/ea76d843c448/pone.0304017.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/caec2ef5bb4e/pone.0304017.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/4ce6e0b8bf2e/pone.0304017.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/ebf9b05ca8d7/pone.0304017.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/1f889179c889/pone.0304017.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/fba5c40c9541/pone.0304017.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/8c63147477f3/pone.0304017.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/20e6d012e3e9/pone.0304017.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/69acaebbc285/pone.0304017.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/8b7497b119d7/pone.0304017.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/ea76d843c448/pone.0304017.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/caec2ef5bb4e/pone.0304017.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/4ce6e0b8bf2e/pone.0304017.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/ebf9b05ca8d7/pone.0304017.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/1f889179c889/pone.0304017.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/fba5c40c9541/pone.0304017.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0404/11175403/8c63147477f3/pone.0304017.g010.jpg

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