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Huang L, Ruan S, Xing Y, Feng M. A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Med Image Anal 2024; 97:103223. [PMID: 38861770 DOI: 10.1016/j.media.2024.103223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/16/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024]
Abstract
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
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Affiliation(s)
- Ling Huang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Su Ruan
- Quantif, LITIS, University of Rouen Normandy, France.
| | - Yucheng Xing
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore
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Li S, Wang H, Meng Y, Zhang C, Song Z. Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation. Phys Med Biol 2024; 69:11TR01. [PMID: 38479023 DOI: 10.1088/1361-6560/ad33b5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/13/2024] [Indexed: 05/21/2024]
Abstract
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning including unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemmas in multi-organ segmentation. We first review the fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.
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Affiliation(s)
- Shiman Li
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China
| | - Haoran Wang
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China
| | - Yucong Meng
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China
| | - Chenxi Zhang
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China
| | - Zhijian Song
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China
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3
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Khawaled S, Freiman M. NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation. Artif Intell Med 2024; 149:102798. [PMID: 38462289 DOI: 10.1016/j.artmed.2024.102798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/26/2023] [Accepted: 02/03/2024] [Indexed: 03/12/2024]
Abstract
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce "NPB-REC", a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation. We use Stochastic Gradient Langevin Dynamics during training to characterize the posterior distribution of the network parameters. This enables us to both improve the quality of the reconstructed images and quantify the uncertainty in the reconstructed images. We demonstrate the efficacy of our approach on a multi-coil MRI dataset from the fastMRI challenge and compare it to the baseline End-to-End Variational Network (E2E-VarNet). Our approach outperforms the baseline in terms of reconstruction accuracy by means of PSNR and SSIM (34.55, 0.908 vs. 33.08, 0.897, p<0.01, acceleration rate R=8) and provides uncertainty measures that correlate better with the reconstruction error (Pearson correlation, R=0.94 vs. R=0.91). Additionally, our approach exhibits better generalization capabilities against anatomical distribution shifts (PSNR and SSIM of 32.38, 0.849 vs. 31.63, 0.836, p<0.01, training on brain data, inference on knee data, acceleration rate R=8). NPB-REC has the potential to facilitate the safe utilization of deep learning-based methods for MRI reconstruction from undersampled data. Code and trained models are available at https://github.com/samahkh/NPB-REC.
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Affiliation(s)
- Samah Khawaled
- The Interdisciplinary program in Applied Mathematics, Faculty of Mathematics, Technion - Israel Institute of Technology, Israel.
| | - Moti Freiman
- The Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Israel.
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Osman YBM, Li C, Huang W, Wang S. Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation. Phys Med Biol 2023; 69:015009. [PMID: 38035374 DOI: 10.1088/1361-6560/ad111b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/30/2023] [Indexed: 12/02/2023]
Abstract
Objective.Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable.Approach.In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods.Significance.Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.
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Affiliation(s)
- Yousuf Babiker M Osman
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, People's Republic of China
| | - Weijian Huang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Peng Cheng Laboratory, Shenzhen 518066, People's Republic of China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, People's Republic of China
- Peng Cheng Laboratory, Shenzhen 518066, People's Republic of China
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Shen N, Xu T, Bian Z, Huang S, Mu F, Huang B, Xiao Y, Li J. SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2476-2489. [PMID: 35862338 DOI: 10.1109/tmi.2022.3193150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkable success in medical image segmentation, they still struggle in the subfield of subcutaneous vessel segmentation due to the scarcity and low-quality of annotated data. To relieve it, this work presents a novel semi-supervised learning framework, SCANet, that achieves accurate vessel segmentation through an alternate training strategy. The SCANet is composed of a multi-scale recurrent neural network that embeds coarse-to-fine features and two auxiliary branches, a consistency decoder and an adversarial learning branch, responsible for strengthening fine-grained details and eliminating differences between ground-truths and predictions, respectively. Equipped with a novel semi-supervised alternate training strategy, the three components work collaboratively, enabling SCANet to accurately segment vessel regions with only a handful of labeled data and abounding unlabeled data. Moreover, to mitigate the shortage of annotated data in this field, we provide a new subcutaneous vessel dataset, VESSEL-NIR. Extensive experiments on a wide variety of tasks, including the segmentation of subcutaneous vessels, retinal vessels, and skin lesions, well demonstrate the superiority and generality of our approach.
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Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation. Artif Intell Med 2022; 138:102476. [PMID: 36990583 DOI: 10.1016/j.artmed.2022.102476] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Medical image segmentation is a fundamental and critical step in many clinical approaches. 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 which is much easier to acquire. Although consistency learning has been proven to be an effective approach by enforcing an invariance of predictions under different distributions, existing approaches cannot make full use of region-level shape constraint and boundary-level distance information from unlabeled data. In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. The framework is guided by the estimated segmentation uncertainty of models to select out relatively certain predictions for consistency learning, so as to effectively exploit more reliable information from unlabeled data. Experiments on two publicly available benchmark datasets showed that: (1) Our proposed method can achieve significant performance improvement by leveraging unlabeled data, with up to 4.13% and 9.82% in Dice coefficient compared to supervised baseline on left atrium segmentation and brain tumor segmentation, respectively. (2) Compared with other semi-supervised segmentation methods, our proposed method achieve better segmentation performance under the same backbone network and task settings on both datasets, demonstrating the effectiveness and robustness of our method and potential transferability for other medical image segmentation tasks.
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Hua Y, Shu X, Wang Z, Zhang L. Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation. Int J Neural Syst 2022; 32:2250016. [DOI: 10.1142/s0129065722500162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by regularizing the learning of limited well-annotated data with the knowledge provided by a large amount of unlabeled data. However, there are many misuses and underutilization of data in conventional semi-supervised methods. On the one hand, the model will deviate from the empirical distribution under the training of numerous unlabeled data. On the other hand, the model treats labeled and unlabeled data differently and does not consider inter-data information. In this paper, a semi-supervised method is proposed to exploit unlabeled data to further narrow the gap between the semi-supervised model and its fully-supervised counterpart. Specifically, the architecture of the proposed method is based on the mean-teacher framework, and the uncertainty estimation module is improved to impose constraints of consistency and guide the selection of feature representation vectors. Notably, a voxel-level supervised contrastive learning module is devised to establish a contrastive relationship between feature representation vectors, whether from labeled or unlabeled data. The supervised manner ensures that the network learns the correct knowledge, and the dense contrastive relationship further extracts information from unlabeled data. The above overcomes data misuse and underutilization in semi-supervised frameworks. Moreover, it favors the feature representation with intra-class compactness and inter-class separability and gains extra performance. Extensive experimental results on the left atrium dataset from Atrial Segmentation Challenge demonstrate that the proposed method has superior performance over the state-of-the-art methods.
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Affiliation(s)
- Yu Hua
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Xin Shu
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Zizhou Wang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
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Rouvière O, Moldovan PC, Vlachomitrou A, Gouttard S, Riche B, Groth A, Rabotnikov M, Ruffion A, Colombel M, Crouzet S, Weese J, Rabilloud M. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation. Eur Radiol 2022; 32:3248-3259. [PMID: 35001157 DOI: 10.1007/s00330-021-08408-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/28/2021] [Accepted: 10/09/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. METHODS The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. RESULTS Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. CONCLUSIONS The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. KEY POINTS • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.
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Affiliation(s)
- Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France. .,Université de Lyon, F-69003, Lyon, France. .,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France. .,INSERM, LabTau, U1032, Lyon, France.
| | - Paul Cezar Moldovan
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, 92156, Suresnes Cedex, France
| | - Sylvain Gouttard
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
| | - Alexandra Groth
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | | | - Alain Ruffion
- Department of Urology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, F-69310, Pierre-Bénite, France
| | - Marc Colombel
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Sébastien Crouzet
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Juergen Weese
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | - Muriel Rabilloud
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
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