MuSIC: Multi-sequential interactive co-registration for cancer imaging data based on segmentation masks | TU Wien

Information

Publication Type: Conference Paper
Workgroup(s)/Project(s): not specified
Date: 2022
ISBN: 978-3-03868-177-9
Publisher: The Eurographics Association
Open Access: yes
Location: Wien
Lecturer: Eduard Gröller
Event: Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM2022)
DOI: 10.2312/vcbm.20221190
Booktitle: VCBM 2022 : Eurographics Workshop on Visual Computing for Biology and Medicine
Pages: 11
Conference date: 22. September 2022 – 23. September 2022
Pages: 81 – 91
Keywords: Visual Computing, Multi-Modal Segmentation, Registration

Abstract

In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future.

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BibTeX

@inproceedings{eichner-2022-music,
title = "MuSIC: Multi-sequential interactive co-registration for
cancer imaging data based on segmentation masks",
author = "Tanja Eichner and Eric M\"{o}rth and Kari Strøno
Wagner-Larsen and Nj{\aa}l Lura and Ingfrid S. Haldorsen and
Eduard Gr\"{o}ller and Stefan Bruckner and Noeska N. Smit",
year = "2022",
abstract = "In gynecologic cancer imaging, multiple magnetic resonance
imaging (MRI) sequences are acquired per patient to reveal
different tissue characteristics. However, after image
acquisition, the anatomical structures can be misaligned in
the various sequences due to changing patient location in
the scanner and organ movements. The co-registration process
aims to align the sequences to allow for multi-sequential
tumor imaging analysis. However, automatic co-registration
often leads to unsatisfying results. To address this
problem, we propose the web-based application MuSIC
(Multi-Sequential Interactive Co-registration). The approach
allows medical experts to co-register multiple sequences
simultaneously based on a pre-defined segmentation mask
generated for one of the sequences. Our contributions lie in
our proposed workflow. First, a shape matching algorithm
based on dual annealing searches for the tumor position in
each sequence. The user can then interactively adapt the
proposed segmentation positions if needed. During this
procedure, we include a multi-modal magic lens visualization
for visual quality assessment. Then, we register the volumes
based on the segmentation mask positions. We allow for both
rigid and deformable registration. Finally, we conducted a
usability analysis with seven medical and machine learning
experts to verify the utility of our approach. Our
participants highly appreciate the multi-sequential setup
and see themselves using MuSIC in the future.",
isbn = "978-3-03868-177-9",
publisher = "The Eurographics Association",
location = "Wien",
event = "Eurographics Workshop on Visual Computing for Biology and
Medicine (VCBM2022)",
doi = "10.2312/vcbm.20221190",
booktitle = "VCBM 2022 : Eurographics Workshop on Visual Computing for
Biology and Medicine",
pages = "11",
pages = "81--91",
keywords = "Visual Computing, Multi-Modal Segmentation, Registration",
URL = "https://www.cg.tuwien.ac.at/research/publications/2022/eichner-2022-music/",
}