Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

TL;DR

This paper introduces a modified 3D U-Net deep learning model with ensemble techniques for accurate sigmoid colon segmentation in CT images, significantly improving precision over baseline models.

Contribution

The study presents a novel combination of a modified 3D U-Net architecture with pyramid pooling, csSE, and ensemble methods to enhance sigmoid colon segmentation accuracy.

Findings

PyP and csSE improve segmentation precision.

Ensemble averaging and voting achieve DSC of 88.11%.

Modified 3D U-Net effectively segments sigmoid colon in CT images.

Abstract

Segmentation of the sigmoid colon is a crucial aspect of treating diverticulitis. It enables accurate identification and localisation of inflammation, which in turn helps healthcare professionals make informed decisions about the most appropriate treatment options. This research presents a novel deep learning architecture for segmenting the sigmoid colon from Computed Tomography (CT) images using a modified 3D U-Net architecture. Several variations of the 3D U-Net model with modified hyper-parameters were examined in this study. Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance. The networks were trained using manually annotated sigmoid colon. A five-fold cross-validation procedure was used on a test dataset to evaluate the network's performance. As indicated by the maximum Dice similarity coefficient (DSC) of 56.92+/-1.42%, the application of PyP and csSE techniques improves segmentation precision. We explored ensemble methods including averaging, weighted averaging, majority voting, and max ensemble. The results show that average and majority voting approaches with a threshold value of 0.5 and consistent weight distribution among the top three models produced comparable and optimal results with DSC of 88.11+/-3.52%. The results indicate that the application of a modified 3D U-Net architecture is effective for segmenting the sigmoid colon in Computed Tomography (CT) images. In addition, the study highlights the potential benefits of integrating ensemble methods to improve segmentation precision.