Atenção Multiescala em Redes U-Net: Uma Abordagem para Segmentação de Rins, Tumores e Cistos em Tomografia Computadorizada

Abstract

Early detection and diagnosis of renal cancer play a crucial role in patient prognosis and treatment, significantly increasing the chances of survival and cure. In this context, advances in radiological imaging have enabled medical specialists to analyze and identify suspicious lesions more effectively. Computed Tomography (CT) stands out as a widely used tool for renal cancer diagnosis due to its ability to generate high-resolution images of internal body structures, including the kidneys, cysts, and tumors. However, manual analysis of these exams is time-consuming and error-prone, often affected by fatigue and distraction, highlighting the need for computational methods to support the automatic segmentation of these structures. In recent years, Convolutional Neural Networks (CNNs) have shown significant potential in this task, providing valuable support to medical imaging specialists. In this study, we propose a convolutional model named Dual-Scale SE U-Net, which leverages multi-scale feature extraction through parallel convolutions (3×3 and 7×7), combined with channel attention mechanisms based on the Squeeze-and-Excitation (SE) module, integrated into the U-Net architecture for the segmentation of kidneys, cysts, and tumors in CT images. As a preprocessing step, the images were resized from 512×512 to 256×256 pixels to improve computational efficiency. The proposed methodology achieved promising results on the KiTS23 dataset, evaluated using five-fold cross-validation, yielding Dice similarity coefficients of 90.94% for Kidneys and Masses, 89.52% for Renal Masses, and 86.27% for Renal Tumors. In the analysis of individual structures, the model achieved 93.80% for kidneys, 92.76% for cysts, and 86.27% for tumors, demonstrating the effectiveness of the proposed approach as a supportive tool for medical diagnosis.