Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Sundaresan, Vaanathi; Zamboni, Giovanna; Dinsdale, Nicola K.; Rothwell, Peter M.; Griffanti, Ludovica; Jenkinson, Mark

doi:10.1016/j.media.2021.102215

Cited by 15 publications

(8 citation statements)

References 57 publications

(85 reference statements)

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“…Network weights are initial-ized by the last epoch of M-train, and further optimized with respect to the new training data. The third strategy is transfer learning [13] ( M-transfer ). It is analogous to retrain strategy but with a different schema of weights’ update.…”

Section: Methodsmentioning

confidence: 99%

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

Riccardi

Ghezzi

Amorosino

et al. 2023

Preprint

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In clinical neuroscience, the segmentation of the main white matter bundles is a mandatory premise for pre-operative neurosurgical planning, neuro-rehabilitation prognosis and monitoring of neuro-related diseases. The automation of the task of bundle segmentation is achieving a good accuracy on healthy individuals taking advantage of data driven approaches and deep learning models. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with model trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how techniques of transfer learning might be beneficial to overcome these limitations. For our analysis, we consider an open publicly available dataset with hundreds of individuals and a clinical dataset with several tens of glioma patients. We focus our preliminary investigation on corticospinal tract. The results show that transfer learning might be effective to overcome the component of domain shift related to systemic bias. Nevertheless, the alterations induced by the tumors are not successfully managed by transfer learning and are still preventing the successful translation of model trained on healthy population.

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Section: Methodsmentioning

confidence: 99%

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

Riccardi

Ghezzi

Amorosino

et al. 2023

Preprint

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“…Each institution provided 20 multi-modal images for the training set. Domain adaptation techniques have been validated on each set of scans acquired at the same institution ( Orbes-Arteaga et al, 2019 , Palladino et al, 2020 , Sundaresan et al, 2021 ). Each institution set (

) is not only used to assess the methods but also to perform domain adaptation during training.…”

Section: Related Workmentioning

confidence: 99%

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Dorent

Kujawa

Ivory

et al. 2023

Medical Image Analysis

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“…Sundaresan et al ( 2021a ) proposed a segmentation approach for white matter hyperintensity lesions that can occur in a variety of brain diseases (including AD, MS, stroke, and small vessel disease). Experiments using domain-adaptation strategies, such as transfer learning, domain adversarial neural networks, and domain unlearning using data from three datasets.…”

Section: Domain Adaptationmentioning

confidence: 99%

“…Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to poor reproducibility across different image domains (Ackaouy et al, 2020). Sundaresan et al (2021a) proposed a segmentation approach for white matter hyperintensity lesions that can occur in a variety of brain diseases (including AD, MS, stroke, and small vessel disease). Experiments using domain-adaptation strategies, such as transfer learning, domain adversarial neural networks, and domain unlearning using data from three datasets.…”

Section: Segmentationmentioning

confidence: 99%

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Bento

Fantini

Park

et al. 2022

Front. Neuroinform.

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Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

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Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Cited by 15 publications

References 57 publications

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

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