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.

使用修改后的 3D U-Net 架构，本研究提出一种新颖的深度学习架构，用于从计算机断层扫描（CT）图像中分割乙状结肠，结果表明采用修改后的架构能有效地进行分割，而集成方法可提高分割精度。