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Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations [chapter]

Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne
2019 Lecture Notes in Computer Science  
In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations  ...  The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation.  ...  Conclusion We proposed a novel semi-supervised segmentation method that learns consistency under transformations.  ... 
doi:10.1007/978-3-030-32226-7_90 fatcat:qpfkog24lvgtxfr56nsz4ptsk4

Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model [article]

Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng
2018 arXiv   pre-print
Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme  ...  Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation.  ...  Semi-supervised segmentation for medical images. Semi-supervised approaches have been applied in various medical imaging tasks. Portela et al.  ... 
arXiv:1808.03887v1 fatcat:bebpxpq6xzbyda5lv36cpio2ja

Semi-supervised Medical Image Segmentation through Dual-task Consistency [article]

Xiangde Luo, Jieneng Chen, Tao Song, Guotai Wang
2021 arXiv   pre-print
Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data.  ...  Meanwhile, our framework outperforms the state-of-the-art semi-supervised medical image segmentation methods. Code is available at:  ...  Yechong Huang for constructive discussions, suggestion and manuscript proofread and also thank the organization teams of MICCAI 2018 left atrial segmentation challenge, the National Institutes of Health  ... 
arXiv:2009.04448v2 fatcat:x7bsdzugcvfdjbyrb4uqxyobr4

Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [article]

Yichi Zhang, Qingcheng Liao, Rushi Jiao, Jicong Zhang
2021 arXiv   pre-print
Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data  ...  Medical image segmentation is a fundamental and critical step in many clinical approaches.  ...  The overview of our proposed uncertainty-guided mutual consistency learning framework for semi-supervised medical image segmentation.  ... 
arXiv:2112.02508v1 fatcat:ofgv42dygvhyxphgh2wbcgdvoy

Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training [article]

Zihang Liu, Chunhui Zhao
2022 arXiv   pre-print
In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation, which is a consistency-based method.  ...  The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data.  ...  RELATED WORK In this section, we first review the typical deep learning-based medical image segmentation methods, then recall the literature of semi-supervised segmentation in medical image analysis, which  ... 
arXiv:2202.06104v1 fatcat:s7yuqfhtebh3jniua5tpkawway

PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning [article]

Xiaoang Shen, Guokai Zhang, Huilin Lai, Jihao Luo, Jianwei Lu, Ye Luo
2021 arXiv   pre-print
To address these problems, we propose a novel semi-supervised FSS framework for medical image segmentation.  ...  Thus, semi-supervised FSS for medical images is accordingly proposed to utilize unlabeled data for further performance improvement.  ...  [19] propose to include surrogate tasks that learn a mapping between noised images and their original counterparts for semi-supervised few-shot medical image segmentation. Li et al.  ... 
arXiv:2108.11694v2 fatcat:zxgpa3rgjvalta3fhtpv74lzxu

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation [article]

Yuexiang Li, Jiawei Chen, Xinpeng Xie, Kai Ma, Yefeng Zheng
2020 arXiv   pre-print
In this paper, we propose a semi-supervised approach to train neural networks with limited labeled data and a large quantity of unlabeled images for medical image segmentation.  ...  Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks for medical image segmentation  ...  We evaluate the proposed semi-supervised learning approach on two medical image segmentation tasks-nuclei segmentation and skin lesion segmentation.  ... 
arXiv:2007.09854v1 fatcat:m3lhohqrrrdddp6723zy2cs4da

DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation [article]

Zhenlin Xu, Marc Niethammer
2019 arXiv   pre-print
Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive.  ...  In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approach can use existing segmentations when available and  ...  Acknowledgements: Research reported in this publication was supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF) under award numbers NSF EECS1711776 and NIH 1R01AR072013  ... 
arXiv:1904.08465v2 fatcat:bhmnx5qysbhyhbzyrlkqjmbaey

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

Jialin Peng, Ye Wang
2021 IEEE Access  
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  application of deep learning models in medical image segmentation.  ...  [183] further considered consistency under extreme transformations, including a diverse set of intensitybased, geometric, and image mixing transformations, and conducted semi-supervised lesion segmentation  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [article]

Yanwen Li, Luyang Luo, Huangjing Lin, Hao Chen, Pheng-Ann Heng
2021 arXiv   pre-print
To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images  ...  Specifically, we present a dual-consistency learning scheme that simultaneously imposes image transformation equivalence and feature perturbation invariance to effectively harness the knowledge from unlabeled  ...  Dual-consistency Learning for Semi-supervised Segmentation Image-level Consistency Learning via transformation equivalence of deep segmentation models f seg indicates that while a transformation T (·)  ... 
arXiv:2104.03225v2 fatcat:shaahzvnafgo5ivx5wptcvcste

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
application of deep learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  [183] further considered consistency under extreme transformations, including a diverse set of intensity-based, geometric, and image mixing transformations, and conducted semi-supervised lesion segmentation  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation [article]

Shumeng Li, Ziyuan Zhao, Kaixin Xu, Zeng Zeng, Cuntai Guan
2021 arXiv   pre-print
Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain.  ...  Extensive experiments have shown that our method achieves competitive performance as compared with full annotation, outperforming other state-of-the-art semi-supervised segmentation methods.  ...  RELATED WORK Semi-supervised learning has achieved promising performance on medical image segmentation under the scarcity of labeled data. Bai et al.  ... 
arXiv:2105.10369v2 fatcat:4xldjc54xbgjxckmdvcmcm35na

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.  ...  We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.  ...  In what follows, we review how transformation consistency has been used in modern semi-supervised learning methods.  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation [article]

Huifeng Yao, Xiaowei Hu, Xiaomeng Li
2022 arXiv   pre-print
This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation.  ...  Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery.  ...  Liu et al. (2021a) also used the Fourier transformation method in federated learning and proves that it is a useful augmentation for medical image segmentation under federated learning.  ... 
arXiv:2201.08657v2 fatcat:cehwtd6imrad3es4a27huu6hny

Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation [article]

Chen Chen, Chen Qin, Cheng Ouyang, Zeju Li, Shuo Wang, Huaqi Qiu, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
2022 arXiv   pre-print
It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning.  ...  The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.  ...  semi-supervised learning.  ... 
arXiv:2108.03429v2 fatcat:m24wykdkbna3fdtq2t5qdlgq2i
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