Adversarial Normalization for Multi Domain Image Segmentation

Delisle, Pierre-Luc; Anctil-Robitaille, Benoit; Desrosiers, Christian; Lombaert, Hervé

doi:10.1109/isbi45749.2020.9098651

Cited by 3 publications

(1 citation statement)

References 34 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [8], the authors again used image translation to synthesise the data and incorporate it with an attention-based neural network. Delisle et al [25] developed an adversarial method to tackle UDA segmentation from a normalisation perspective. Feature-level domain adaptation: In the feature-level, Kamnitsas et al [26] proposed a UDA for brain lesion segmentation, which learned domain-invariant features with a discriminator that predicts the input image domain.…”

Section: Related Workmentioning

confidence: 99%

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Vesal

Kosti

et al. 2021

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and timeconsuming task, which also suffers from high inter-and intra-observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-toend framework that integrates: a) entropy minimisation, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Lategadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Vesal

Kosti

et al. 2021

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

Deep learning model-based segmentation of medical diseases from MRI and CT images

Murmu

Kumar

2021

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)

View full text Add to dashboard Cite

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

show abstract

Adversarial Normalization for Multi Domain Image Segmentation

Cited by 3 publications

References 34 publications

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Deep learning model-based segmentation of medical diseases from MRI and CT images

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

Contact Info

Product

Resources

About