Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment

Feng, Wei; Ju, Lie; Wang, Lin; Song, Kaimin; Zhao, Xin; Ge, Zongyuan

doi:10.1609/aaai.v37i1.25138

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…There are UDA approaches dealing with histological images, i.e., for general whole-slide images [94] or specific to the classification of breast cancer images [95]. Further advancements bridging domains of different imaging modalities are made [96] by, e.g., style adaptation [97] or selective entropy constraints [98] where pixels are first categorized in reliable and unreliable before the domain shift. However, all these approaches rely on a well-labeled source domain.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Glänzer,

Masalkhi,

Roeth

et al. 2023

Cancers

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Semantic segmentation is an important imaging analysis method enabling the identification of tissue structures. Histological image segmentation is particularly challenging, having large structural information while providing only limited training data. Additionally, labeling these structures to generate training data is time consuming. Here, we demonstrate the feasibility of a semantic segmentation using U-Net with a novel sparse labeling technique. The basic U-Net architecture was extended by attention gates, residual and recurrent links, and dropout regularization. To overcome the high class imbalance, which is intrinsic to histological data, under- and oversampling and data augmentation were used. In an ablation study, various architectures were evaluated, and the best performing model was identified. This model contains attention gates, residual links, and a dropout regularization of 0.125. The segmented images show accurate delineations of the vascular structures (with a precision of 0.9088 and an AUC-ROC score of 0.9717), and the segmentation algorithm is robust to images containing staining variations and damaged tissue. These results demonstrate the feasibility of sparse labeling in combination with the modified U-Net architecture.

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Section: Discussion and Future Workmentioning

confidence: 99%

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Glänzer,

Masalkhi,

Roeth

et al. 2023

Cancers

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“…Unlike other methods, we consider two scenarios of labels and choose appropriate methods to optimize the model, rather than only selecting samples with high confidence for training [40]. Our selective minimax entropy objective L SM M E is given by:…”

Section: Selective Minmax Entropy Based On Feature Cluster Consistencymentioning

confidence: 99%

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

Gou,

Cui

2024

Preprint

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Medical imaging approaches widely employ deep neural networks for the investigation and diagnosis of different skin disorders. However, recent studies suggest that even a proficient model based on deep learning might struggle with generalization when applied to datasets from disparate cohorts due to domain shift phenomena. Meanwhile, there are usually need many well-labelled images utilized for the training process to attain a stronger level of performance. In order to alleviate the domain shift and the necessity for adequate training data, we introduce a novel method termed as adversarial selective domain adaption with feature cluster (ASDA). It achieves effective performance improvement of model when the target dataset is smaller than the source dataset. Specifically, we generate a set of feature clusters for each sample in the target domain to alleviate the demand for data. Subsequently, a conditional domain adversarial network is used to mitigate domain shift. Finally, due to consistency issues between feature clusters and samples, we propose a method of selective minmax entropy to maintain consistency. Our method diverges from typical domain adaption approaches that solely target reducing the domain gap. Instead, we address both the discrepancy between domains and the problem of limited data in the target dataset simultaneously. Extensive experiments have been undertaken on datasets pertaining to skin cancer, that confirms ASDA's efficacy in skin cancer diagnosis for dermatoscopic and clinic image.

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Adaptive wavelet‐VNet for single‐sample test time adaptation in medical image segmentation

Qian,

Lu,

Zhang

2024

Medical Physics

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BackgroundIn medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning‐based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine‐tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test‐time adaptation (TTA) presents a more practical but challenging solution.PurposeTo improve the segmentation accuracy of deep learning‐based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains.MethodsIn this study, we proposed to dynamically adapt a wavelet‐VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V‐Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape‐aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains).ResultsThe proposed method showed the highest segmentation accuracy among all methods, achieving a mean (± SD) Dice coefficient (DSC) of 78.10 ± 5.23% and a mean 95th Hausdorff distance (HD95) of 15.52 ± 5.84 mm on the liver dataset; and a mean DSC of 80.02 ± 3.89% and a mean HD95 of 9.18 ± 3.47 mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively.ConclusionsThe proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi‐objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet.

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Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment

Cited by 11 publications

References 29 publications

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

Adaptive wavelet‐VNet for single‐sample test time adaptation in medical image segmentation

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