Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations

Hou, Jinyong; Ding, Xuejie; Deng, Jeremiah D.

doi:10.1109/wacv51458.2022.00183

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…The approach utilizes a discriminator to generate confidence maps and incorporates an auxiliary discriminator to aid the primary discriminator, which may face performance limitations due to the scarcity of labeled data. Hou et al [19] proposed a semi-supervised medical image segmentation method that employs a Leaking GAN (generative adversarial network) to contaminate the discriminator by leaking information from the generator, thereby promoting better learning of the discriminator. Wu et al [20] introduced a semi-supervised segmentation method that augments the inter-class distance within the feature space by means of feature gradient map regularization.…”

Section: Semi-supervised Medical Image Segmentationmentioning

confidence: 99%

Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning

Zhao,

Wang

2024

J. Imaging

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In the realm of medical image analysis, the cost associated with acquiring accurately labeled data is prohibitively high. To address the issue of label scarcity, semi-supervised learning methods are employed, utilizing unlabeled data alongside a limited set of labeled data. This paper presents a novel semi-supervised medical segmentation framework, DCCLNet (deep consistency collaborative learning UNet), grounded in deep consistent co-learning. The framework synergistically integrates consistency learning from feature and input perturbations, coupled with collaborative training between CNN (convolutional neural networks) and ViT (vision transformer), to capitalize on the learning advantages offered by these two distinct paradigms. Feature perturbation involves the application of auxiliary decoders with varied feature disturbances to the main CNN backbone, enhancing the robustness of the CNN backbone through consistency constraints generated by the auxiliary and main decoders. Input perturbation employs an MT (mean teacher) architecture wherein the main network serves as the student model guided by a teacher model subjected to input perturbations. Collaborative training aims to improve the accuracy of the main networks by encouraging mutual learning between the CNN and ViT. Experiments conducted on publicly available datasets for ACDC (automated cardiac diagnosis challenge) and Prostate datasets yielded Dice coefficients of 0.890 and 0.812, respectively. Additionally, comprehensive ablation studies were performed to demonstrate the effectiveness of each methodological contribution in this study.

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Section: Semi-supervised Medical Image Segmentationmentioning

confidence: 99%

Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning

Zhao,

Wang

2024

J. Imaging

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“…SSL in Segmentation: In semi-supervised image segmentation, consistency regularisation is commonly used [5] , [15] , [16] , [17] , [18] , [19] where different data augmentation techniques are applied at the input level. Another related work [8] forces the model to learn rotation invariant predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Existing consistency regularisation methods [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] are mainly focusing on producing predictions which are invariant against different input level perturbations. In other words, we can interpret that consistency regularisation methods aim at training networks which generate confidence invariant predictions.…”

Section: Introductionmentioning

confidence: 99%

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations With Limited Labels

Xu¹,

Zhou²,

Chen

et al. 2023

IEEE Trans. Med. Imaging

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Semi-supervised learning (SSL) is a promising machine learning paradigm to address the ubiquitous issue of label scarcity in medical imaging. The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations. However, image level perturbations violate the cluster assumption in the setting of segmentation. Moreover, existing image level perturbations are hand-crafted which could be sub-optimal. In this paper, we propose MisMatch, a semi-supervised segmentation framework based on the consistency between paired predictions which are derived from two differently learnt morphological feature perturbations. MisMatch consists of an encoder and two decoders. One decoder learns positive attention for foreground on unlabelled data thereby generating dilated features of foreground. The other decoder learns negative attention for foreground on the same unlabelled data thereby generating eroded features of foreground. We normalise the paired predictions of the decoders, along the batch dimension. A consistency regularisation is then applied between the normalised paired predictions of the decoders. We evaluate MisMatch on four different tasks. Firstly, we develop a 2D U-net based MisMatch framework and perform extensive cross-validation on a CT-based pulmonary vessel segmentation task and show that MisMatch statistically outperforms state-of-the-art semi-supervised methods. Secondly, we show that 2D MisMatch outperforms state-of-the-art methods on an MRI-based brain tumour segmentation task. We then further confirm that 3D V-net based MisMatch outperforms its 3D counterpart based on consistency regularisation with input level perturbations, on two different tasks including, left atrium segmentation from 3D CT images and whole brain tumour segmentation from 3D MRI images. Lastly, we find that the performance improvement of MisMatch over the baseline might originate from its better calibration. This also implies that our proposed AI system makes safer decisions than the previous methods.

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“…Nie et al [98] propose to adversarially train the segmentation network based on the confidence map from the confidence network and a region-attention based semisupervised learning strategy to utilize unlabeled data for training. Hou et al [99] add a leaking GAN into the semisupervised framework which can pollute the discriminator by leaking information from the generator for more moderate generations. Chaitanya et al [100] propose a novel task-driven data augmentation method to synthesize new training examples, where a generative network explicitly applies deformation fields and additional strength masks to model shape and strength changes.…”

Section: Unsupervised Regularization With Adversarial Learningmentioning

confidence: 99%

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Jiao¹,

Zhang²,

Liu³

et al. 2022

Preprint

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Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.

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Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations

Cited by 14 publications

References 27 publications

Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning

Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations With Limited Labels

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

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