3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images

no code implementations • 17 Mar 2020 • Cheryl Sital, Tom Brosch, Dominique Tio, Alexander Raaijmakers, Jürgen Weese

The proposed learning strategies improved results in $75\%$ of the experiments compared to training from scratch and increased the dice score by up to $0. 040$ and $0. 024$ for a ratio of unlabeled to labeled training data of about $32 : 1$ and $12. 5 : 1$, respectively.

Image Segmentation Liver Segmentation +3

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

no code implementations • 11 Sep 2018 • Danielle F. Pace, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese, Mehdi H. Moghari, Polina Golland

We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels.

Segmentation