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Segmentation of the sigmoid colon is a crucial aspect of treatingdiverticulitis. It enables accurate identification and localisation ofinflammation, which in turn helps healthcare professionals make informeddecisions about the most appropriate treatment options. This research presentsa novel deep learning architecture for segmenting the sigmoid colon fromComputed Tomography (CT) images using a modified 3D U-Net architecture. Severalvariations of the 3D U-Net model with modified hyper-parameters were examinedin this study. Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation(csSE) were also used to improve the model performance. The networks weretrained using manually annotated sigmoid colon. A five-fold cross-validationprocedure was used on a test dataset to evaluate the network's performance. Asindicated by the maximum Dice similarity coefficient (DSC) of 56.92+/-1.42%,the application of PyP and csSE techniques improves segmentation precision. Weexplored ensemble methods including averaging, weighted averaging, majorityvoting, and max ensemble. The results show that average and majority votingapproaches with a threshold value of 0.5 and consistent weight distributionamong the top three models produced comparable and optimal results with DSC of88.11+/-3.52%. The results indicate that the application of a modified 3D U-Netarchitecture is effective for segmenting the sigmoid colon in ComputedTomography (CT) images. In addition, the study highlights the potentialbenefits of integrating ensemble methods to improve segmentation precision.

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