Unsupervised Domain Adaptation in Semantic Segmentation: A Review

Toldo, Marco; Maracani, Andrea; Michieli, Umberto; Zanuttigh, Pietro

doi:10.3390/technologies8020035

Cited by 145 publications

(67 citation statements)

References 116 publications

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“…Recently, auxiliary tasks, such as the adaptation of a well-trained model from a similar domain with a similar task [11], [41], have been leveraged to migrate this problem. Although we will not cover the unsupervised segmentation and their solutions, such as unsupervised domain adaptation (UDA) [42] and zero-shot learning [43], we mention it here to start by looking at all settings in the big picture. In this paper, we focus on methods that learn to segment medical images with incomplete, inexact, and inaccurate annotations by jointly leveraging a few labeled data and a large number of unlabeled examples.…”

Section: Image Segmentation With Limited Supervisionmentioning

confidence: 99%

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Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Wang

2021

IEEE Access

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Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.

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Section: Image Segmentation With Limited Supervisionmentioning

confidence: 99%

“…Thus, pretraining on relevant domains and applying to the current domain with supervised or semi-supervised training, known as Domain Adaptation [219] or Domain generalization, has received growing attention. Please refer to [42] for comprehensive reviews of domain adaptation for semantic segmentation.…”

Section: K Summarymentioning

confidence: 99%

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Wang

2021

IEEE Access

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“…In this section, we will give a brief review of related works in both areas. A more detailed review of UDA for image segmentation can be found in [18]- [22]. In the field of UDA, early research mainly focused on aligning the distributions of feature space by minimizing distance measurements.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

Gong

Sun

et al. 2021

IEEE Access

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With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely affect model performance when deployed to unseen data with heterogeneous features. To alleviate this problem, we present a novel unsupervised domain adaptation network, for generalizing models learned from the labeled source domain to the unlabeled target domain for cross-modality biomedical image segmentation. Specifically, our approach consists of two key modules, a conditional domain discriminator (CDD) and a category-centric prototype aligner (CCPA). The CDD, extended from conditional domain adversarial networks in classifier tasks, is effective and robust in handling complex cross-modality biomedical images. The CCPA, improved from the graph-induced prototype alignment mechanism in cross-domain object detection, can exploit precise instance-level features through an elaborate prototype representation. In addition, it can address the negative effect of class imbalance via entropy-based loss. Extensive experiments on a public benchmark for the cardiac substructure segmentation task demonstrate that our method significantly improves performance on the target domain. INDEX TERMS Biomedical image segmentation, cross-modality learning, unsupervised domain adaptation, category-centric prototype aligner.

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“…However, such global feature alignment does not necessarily result in the intraclassly correct semantic representation of the target domain. 29 In multiple class segmentation, it almost fails to achieve substantial gains over the baseline that is trained with data augmentation and registration. 42 For some small-gap UDA tasks of medical image segmentation, the generative-based approaches are wildly used.…”

Section: Introductionmentioning

confidence: 99%

IAS‐NET: Joint intraclassly adaptive GAN and segmentation network for unsupervised cross‐domain in neonatal brain MRI segmentation

You

Wang

et al. 2021

Medical Physics

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In neonatal brain magnetic resonance image (MRI) segmentation, the model we trained on the training set (source domain) often performs poorly in clinical practice (target domain). As the label of target-domain images is unavailable, this cross-domain segmentation needs unsupervised domain adaptation (UDA) to make the model adapt to the target domain. However, the shape and intensity distribution of neonatal brain MRI images across the domains are largely different from adults'. Current UDA methods aim to make synthesized images similar to the target domain as a whole. But it is impossible to synthesize images with intraclass similarity because of the regional misalignment caused by the cross-domain difference. This will result in generating intraclassly incorrect intensity information from target-domain images. To address this issue, we propose an IAS-NET (joint intraclassly adaptive generative adversarial network (GAN) (IA-NET) and segmentation) framework to bridge the gap between the two domains for intraclass alignment. Methods: Our proposed IAS-NET is an elegant learning framework that transfers the appearance of images across the domains from both image and feature perspectives. It consists of the proposed IA-NET and a segmentation network (S-NET). The proposed IA-NET is a GAN-based adaptive network that contains one generator (including two encoders and one shared decoder) and four discriminators for cross-domain transfer. The two encoders are implemented to extract original image, mean, and variance features from source and target domains. The proposed local adaptive instance normalization algorithm is used to perform intraclass feature alignment to the target domain in the feature-map level. S-NET is a U-net structure network that is used to provide semantic constraint by a segmentation loss for the training of IA-NET. Meanwhile, it offers pseudo-label images for calculating intraclass features of the target domain. Source code (in Tensorflow) is available at https://github.com/lb-whu/RAS-NET/. Results: Extensive experiments are carried out on two different data sets (Neo-BrainS12 and dHCP), respectively. There exist great differences in the shape, size, and intensity distribution of magnetic resonance (MR) images in the two databases. Compared to baseline, we improve the average dice score of all tissues on NeoBrains12 by 6% through adaptive training with unlabeled dHCP images. Besides, we also conduct experiments on dHCP and improved the average dice score by 4%. The quantitative analysis of the mean and variance of the synthesized images shows that the synthesized image by the proposed is closer to the target domain both in the full brain or within each class than that of the compared methods.

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Unsupervised Domain Adaptation in Semantic Segmentation: A Review

Cited by 145 publications

References 116 publications

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

IAS‐NET: Joint intraclassly adaptive GAN and segmentation network for unsupervised cross‐domain in neonatal brain MRI segmentation

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