TransMorph: Transformer for unsupervised medical image registration

1 code implementation • 19 Nov 2021 • Junyu Chen, Eric C. Frey, Yufan He, William P. Segars, Ye Li, Yong Du

Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.

Image Registration Medical Image Registration

iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

no code implementations • 20 Aug 2020 • Wanyi Fu, Shobhit Sharma, Ehsan Abadi, Alexandros-Stavros Iliopoulos, Qi. Wang, Joseph Y. Lo, Xiaobai Sun, William P. Segars, Ehsan Samei

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration

no code implementations • 2 Dec 2020 • Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch, Christian Tönnes, William P. Segars, Lothar R. Schad, Frank G. Zöllner, Alena-Kathrin Golla

We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.

Computed Tomography (CT) Image Registration +3

Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT

no code implementations • 3 Mar 2022 • Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu, Ehsan Abadi, Maciej A. Mazurowski, Ehsan Samei, William P. Segars, Joseph Y. Lo

This demonstrates that quality data is the key to improving the model's performance.

Organ Segmentation Segmentation