Segmentação automática do pâncreas em tomografias computadorizadas abdominais utilizando uma abordagem orientada a atlas probabilístico e aprendizado profundo

Abstract

Automatic segmentation of the pancreas in Computed Tomography (CT) images is a challenging task due to the organ’s high anatomical variability, low contrast between adjacent tissues, and the large amount of data present in volumetric examinations. This task is important for various clinical applications, such as computer-aided diagnosis, treatment planning, and quantitative analysis of anatomical structures. However, manual identification of the pancreas in CT scans can demand significant time from specialists, making the process susceptible to variations between observers. In this context, computational methods based on deep learning have been widely used to assist in the automatic analysis of medical images. Thus, this work proposes an automated method for pancreas segmentation in abdominal CT images. The developed method consists of different stages, including image preprocessing, automatic filtering of slices without the organ using a convolutional neural network, extraction of the region of interest using a probabilistic atlas, and application of deep learning models to perform the segmentation. For this stage, different neural network architectures were trained, including DeepLabV3, U-Net++, and SegFormer. Subsequently, the results generated by the models were combined using an ensemble strategy to increase the robustness of the segmentation. The method was evaluated using the Medical Segmentation Decathlon database, composed of abdominal CT scans manually annotated by specialists. The experimental results demonstrated that the proposed ensemble achieved superior performance to the individual models, obtaining a Dice coefficient of 78.55%, an IoU of 68.17%, and a Recall of 84.41%. Thus, the results obtained indicate that the proposed approach has the potential to assist specialists in the analysis of CT scans, contributing to the development of diagnostic support systems based on deep learning.