Research Repository

Multimodal cardiac segmentation using disentangled representation learning.

Chartsias, Agisilaos and Papanastasiou, Giorgos and Wang, Chegnjia and Stirrat, Colin and Semple, Scott and Newby, David and Dharmakumar, Rohan and Tsaftaris, Sotirios (2020) Multimodal cardiac segmentation using disentangled representation learning. In: STACOM: International Workshop on Statistical Atlases and Computational Models of the Heart, 2019-10-19 - 2019-10-19, Shenzhen, China.

Chartsias, Papanastasiou, et al. MICCAI (LNCS) 2019.pdf - Accepted Version

Download (1MB) | Preview


Magnetic Resonance (MR) protocols use several sequences to evaluate pathology and organ status. Yet, despite recent advances, the analysis of each sequence’s images (modality hereafter) is treated in isolation. We propose a method suitable for multimodal and multi-input learning and analysis, that disentangles anatomical and imaging factors, and combines anatomical content across the modalities to extract more accurate segmentation masks. Mis-registrations between the inputs are handled with a Spatial Transformer Network, which non-linearly aligns the (now intensity-invariant) anatomical factors. We demonstrate applications in Late Gadolinium Enhanced (LGE) and cine MRI segmentation. We show that multi-input outperforms single-input models, and that we can train a (semi-supervised) model with few (or no) annotations for one of the modalities. Code is available at

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers Part of the Lecture Notes in Computer Science book series (LNCS, volume 12009) Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 12009)
Uncontrolled Keywords: Multimodal segmentation; Disentanglement; Representation learning; Cardiac MR
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Aug 2020 11:34
Last Modified: 23 Sep 2022 19:41

Actions (login required)

View Item View Item