# Automatic segmentation of kidney and liver tumors in CT images

## TL;DR

This paper presents a CNN-based method for automatic segmentation of kidney and liver tumors in CT images, achieving high accuracy on multiple datasets and challenges, emphasizing a careful learning approach over complex architectures.

## Contribution

The study introduces a focused learning strategy for CNNs that improves segmentation accuracy without relying on complex network architectures.

## Key findings

- Achieved 78.8% DICE score on 3DIRCADb dataset.
- Obtained 96.38% kidney Dice score in KiTS-2019 challenge.
- Achieved 67.38% tumor Dice score in KiTS-2019 challenge.

## Abstract

Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores.