Comparative Study of Deep Learning Models for the Segmentation of Large Intestine from the Computed Tomography Colonography images

Author(s)	PRERANA C RAO
Country	India
Abstract	Traditionally, methods such as thresholding, region expanding, and conventional convolutional neuralnetworks (CNNs) have been used for the segmentation of large intestine images. These techniquesfrequently have trouble detecting complicated diseases like inflammation or polyps, as well as variationsin colon morphology and image quality. The inherent difficulties of dealing with low contrast, irregularboundaries, and noisy medical images often restricted the accuracy of segmentation. In this project, weare using deep learning architectures, namely UNet and Mask R-CNN, for precise large intestinesegmentation from CT colonography (CTC) images is examined in this work. After that, research isconducted and used to evaluate how well these deep learning models perform based on their dice score.The findings offer important new information about the best DL models for large intestine segmentationin CTC, which can also help with large intestine related disease diagnosis also.
Keywords	CTC, DL, Large Intestine Segmentation, UNet, Mask R-CNN
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.41104
Short DOI	https://doi.org/g9fccp

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International Journal For Multidisciplinary Research