Automatic colorectal segmentation with convolutional neural network

被引：1

作者：

Guachi L. ^{[1
]}

Guachi R. ^{[2
]}

Bini F. ^{[2
]}

Marinozzi F. ^{[2
]}

机构：

[1] Yachay Tech University, Ecuador

[2] Sapienza University of Rome, Italy

来源：

Computer-Aided Design and Applications | 2019年 / 16卷 / 05期

关键词：

Colon Segmentation; Convolutional neural network; Tissues segmentation;

D O I：

10.14733/cadaps.2019.836-845

中图分类号：

学科分类号：

摘要：

This paper presents a new method for colon tissues segmentation on Computed Tomography images which takes advantages of using deep and hierarchical learning about colon features through Convolutional Neural Networks (CNN). The proposed method works robustly reducing misclassified colon tissues pixels that are introduced by the presence of noise, artifacts, unclear edges, and other organs or different areas characterized by the same intensity value as the colon. Patch analysis is exploited for allowing the classification of each center pixel as colon tissue or background pixel. Experimental results demonstrate the proposed method achieves a higher effectiveness in terms of sensitivity and specificity with respect to three state-of the art methods. © 2019 CAD Solutions, LLC.

引用

页码：836 / 845

页数：9

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