Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparison of Unet3D Models for Kidney Tumor Segmentation

Version 1 : Received: 25 January 2020 / Approved: 26 January 2020 / Online: 26 January 2020 (08:04:01 CET)

How to cite: Turk, F.; Luy, M.; Barisci, N. Comparison of Unet3D Models for Kidney Tumor Segmentation. Preprints 2020, 2020010314 (doi: 10.20944/preprints202001.0314.v1). Turk, F.; Luy, M.; Barisci, N. Comparison of Unet3D Models for Kidney Tumor Segmentation. Preprints 2020, 2020010314 (doi: 10.20944/preprints202001.0314.v1).

Abstract

Worldwide, hundreds of thousands of people are diagnosed with kidney cancer and this disease is more common in developed and industrialized countries. Previously, kidney cancer was known as an elderly disease and was seen in people over a certain age; nowadays it is also seen in younger individuals and it is easier to diagnose thanks to new radiological diagnostic methods. A kidney tumor is a type of cancer that is extremely aggressive and needs surgical treatment rapidly. Today, approximately 30% of patients diagnosed with kidney cancer are unfortunately noticed at the stage of metastatic disease (spread to distant organs). The biggest factor that pushes us to this study is that kidney tumors progress unlike other cancer types with little or no symptoms. Therefore, conducting such studies is extremely important for early diagnosis. In this study, we compare the Unet3D models in order to help people who are dealing with difficulties in the diagnosis of kidney cancer. Unet, Unet+ResNet and Unet++ models were compared for image segmentation.

Subject Areas

kidney tumor; renal tumor; Unet3D; Unet+ResNet; Unet++ segmentation

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