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Journal article Open Access

3-fold Cross-Validation for Kidney Tumor Segmentation on the KiTS19 dataset via MIScnn

Müller, Dominik; Kramer, Frank

We performed a 3-fold Cross-Validation on the Kidney Tumor Segmentation Challenge 2019 dataset (KITS19) with our newly developed framework for medical image segmentation. MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.

The aim of MIScnn is to provide an intuitive API allowing fast building of medical
image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art
deep learning models and model utilization like training, prediction as well as fully automatic evaluation (e.g. cross-validation).
Even so, high configurability and multiple open interfaces allow full pipeline customization. MIScnn is based on Keras with Tensorflow as backend.\
More information about MIScnn can be found in the publication or on the Git repository: https://github.com/frankkramer-lab/MIScnn

The task of the KITS19 challenge was to compute a semantic segmentation of arterial phase abdominal CT scans from 300 kidney cancer patients. Each pixel had to be labeled into one of three classes: Background, kidney or tumor. The original scans have an image resolution of 512x512 and on average 216 slices (highest slice number is 1059).

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evaluation.zip
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log.txt
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MIScnn_KiTS19.ipynb
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