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  Convolutional neural network stacking for medical image segmentation in CT scans

Kloenne, M., Niehaus, S., Lampe, L., Merola, A., Reinelt, J., & Scherf, N. (2019). Convolutional neural network stacking for medical image segmentation in CT scans. In 2019 Kidney Tumor Segmentation Challenge. Minneapolis, MN: University of Minnesota Libraries Publishing. doi:10.24926/548719.090.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-D45B-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-D45C-5
Genre: Conference Paper

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 Creators:
Kloenne, Marie 1, Author
Niehaus, Sebastian 1, Author
Lampe, Leonie1, Author
Merola, Alberto1, Author              
Reinelt, Janis1, Author              
Scherf, Nico1, 2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Free keywords: Medical image segmentation ; Computed Tomography (CT)·Kidney tumor segmentation
 Abstract: Computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs). The main challenges in handling CT scans with CNN are the scale of data (large range of Hounsfield Units) and the processing of the slices. In this paper, we consider a framework, which addresses these demands regarding the data preprocessing, the data augmentation, and the CNN architecture itself. For this purpose, we present a data preprocessing and an augmentation method tailored to CT data. We evaluate and compare different input dimensionalities and two different CNN architectures. One of the architectures is a modified U-Net and the other a modified Mixed-Scale Dense Network (MS-D Net). Thus, we compare dilated convolutions for parallel multi-scale processing to the U-Net approach with traditional scaling operations based on the different input dimensionalities. Finally, we combine a set of 3D modified MS-D Nets and a set of 2D modified U-Nets as a stacked CNN-model to combine the different strengths of both model.

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Language(s): eng - English
 Dates: 2019-01
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.24926/548719.090
 Degree: -

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Title: MICCAI 2019
Place of Event: Shenzhen, China
Start-/End Date: 2019-10-13 - 2019-10-19

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Title: 2019 Kidney Tumor Segmentation Challenge
Source Genre: Proceedings
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Publ. Info: Minneapolis, MN : University of Minnesota Libraries Publishing
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -