Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers

no code implementations • 26 Jan 2022 • Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, Sylvia Saalfeld

With Dice scores of averaged 98+-2% for liver and 81+-28% lesion segmentation on the MRI dataset and 97+-2% and 79+-25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging.

Computed Tomography (CT) Image Segmentation +3

MedMeshCNN -- Enabling MeshCNN for Medical Surface Models

no code implementations • 10 Sep 2020 • Lisa Schneider, Annika Niemann, Oliver Beuing, Bernhard Preim, Sylvia Saalfeld

Conclusions: These results demonstrate that MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes.

Benchmarking Segmentation

Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks

no code implementations • 8 Jan 2020 • Georg Hille, Johannes Steffen, Max Dünnwald, Mathias Becker, Sylvia Saalfeld, Klaus Tönnies

This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach.

Segmentation Specificity