Automatic system for the renal and cancer segmentation in CT images

Authors
Les, T
Markiewicz, T
Dziekiewicz, M
Lorent, M

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
This article presents the concept of a complex system for automatic detection of kidneys and kidney tumors in computed tomography images. An effective treatment of cancer depends on a quick and effective diagnosis. Computer support for medical diagnostics is crucial in effective specialists’ analysis. Automatic and accurate location, together with precise detection of the kidney and/or tumor contour is a demanding task. In this article, authors present a complex system for automatic detection of kidneys and kidney tumors, based on machine learning techniques, using the U-Net network. Convolutional neural network recognition results are then processed in multiple stages, using morphological processing, 3D model analysis, geometric coefficients analysis and region-growth implementation. The results of the system detection were compared to the reference images marked by an expert. The system presented in the article is characterized by a very high efficiency of recognition and segmentation of kidney and tumor areas.

Identifiers
doi: 10.24926/548719.092