Artificial intelligence in oncourology: integrated deep learning technologies in the tasks of segmentation of three-dimensional images of kidney tumors

Abstract

In order to improve the accuracy of cancer diagnosis, a new convolutional neural network architecture is presented, which provides automatic segmentation and detection of kidney tumors on three-dimensional images obtained by computed tomography. The proposed approach is based on the integration of three complementary technologies: multilevel convolutional processing, residual connections and U-Net architectural principles. This approach ensures efficient processing of voluminous medical data. An original neural network system for segmentation of kidney images obtained by computed tomography and detection of kidney tumors has been built. To validate the system, a comprehensive experiment was conducted using the publicly available KiTS19 dataset (Kidney Tumor Segmentation 2019) provided by the University of Minnesota Clinic through the Grand Challenge platform. The dataset includes 300 labeled images of kidneys obtained by computed tomography, with confirmed diagnoses. The experiment consisted of the following stages: dataset preprocessing, including normalization and augmentation; system training for 210 cases; validation on an independent sample of 90 cases. The results of the experiment demonstrate the high diagnostic efficiency of the system: the accuracy of automatic segmentation of anatomical structures of the kidneys was 96 % (according to the Dice coefficient); the accuracy of detection and segmentation of tumor formations has reached 91 % (according to the Dice coefficient). The results obtained can be applied in the following areas of clinical practice: preoperative planning and navigation during organ – preserving operations; automated screening of studies using computed tomography for early detection of kidney tumors; quantitative assessment of the dynamics of tumor growth in monitoring the course of the disease; support for clinical decision-making in oncourology.

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