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Zhang T, Wei D, Zhu M, Gu S, Zheng Y. Self-supervised learning for medical image data with anatomy-oriented imaging planes. Med Image Anal 2024; 94:103151. [PMID: 38527405 DOI: 10.1016/j.media.2024.103151] [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: 04/07/2023] [Revised: 12/29/2023] [Accepted: 03/20/2024] [Indexed: 03/27/2024]
Abstract
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.
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Affiliation(s)
- Tianwei Zhang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Wei
- Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518057, China
| | - Mengmeng Zhu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Yefeng Zheng
- Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518057, China
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Wei D, Huang Y, Lu D, Li Y, Zheng Y. Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views. Med Phys 2024; 51:1832-1846. [PMID: 37672318 DOI: 10.1002/mp.16692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. PURPOSE Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning. METHODS The system mines the spatial relationship-more specifically, locates the intersecting lines-between the target planes and source views, and trains U-Net-based deep networks to regress heatmaps defined by distances from the intersecting lines. On the one hand, the intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. On the other hand, as the spatial relationship is self-contained in properly stored data, for example, in the DICOM format, the need for additional manual annotation is eliminated. In addition, the interplay of the multiple target planes predicted in a source view is utilized in a stacked hourglass architecture consisting of repeated U-Net-style building blocks to gradually improve the regression. Then, a multiview planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. For performance evaluation, the retrospectively identified planes prescribed by the technologists are used as the ground truth, and the plane angle differences and localization distances between the planes prescribed by our system and the ground truth are compared. RESULTS The retrospective experiments include 181 clinical CMR exams, which are randomly split into training, validation, and test sets in the ratio of 64:16:20. Our system yields the mean angular difference and point-to-plane distance of 5.68∘ $^\circ$ and 3.12 mm, respectively, on the held-out test set. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout. CONCLUSIONS The proposed system demonstrates accurate automatic CMR view plane prescription based on deep learning on properly archived data, without the need for further manual annotation. This work opens a new direction for automatic view planning of anatomy-oriented medical imaging beyond CMR.
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Affiliation(s)
- Dong Wei
- Tencent Jarvis Lab, Shenzhen, China
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Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks. ALGORITHMS 2021. [DOI: 10.3390/a14080249] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.
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Engblom H, Tufvesson J, Jablonowski R, Carlsson M, Aletras AH, Hoffmann P, Jacquier A, Kober F, Metzler B, Erlinge D, Atar D, Arheden H, Heiberg E. A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data. J Cardiovasc Magn Reson 2016; 18:27. [PMID: 27145749 PMCID: PMC4855857 DOI: 10.1186/s12968-016-0242-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/20/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. METHODS The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). RESULTS Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). CONCLUSIONS The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CLINICAL TRIAL REGISTRATION CHILL-MI: NCT01379261 . MITOCARE NCT01374321 .
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Affiliation(s)
- Henrik Engblom
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Jane Tufvesson
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
- />Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Robert Jablonowski
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Marcus Carlsson
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Anthony H. Aletras
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
- />Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Pavel Hoffmann
- />Section for Interventional Cardiology, Department of Cardiology, Division of Cardiovascular and Pulmonary Diseases, Oslo University Hospital, Ullevaal, Oslo, Norway
| | - Alexis Jacquier
- />Aix-Marseille University, UMR 7339 CRMBM, Marseille, France
- />Department of Radiology, La Timone University Hospital, Marseille, France
| | - Frank Kober
- />Aix-Marseille University, UMR 7339 CRMBM, Marseille, France
| | - Bernhard Metzler
- />Department of Cardiology, Medical University of Innsbruck, Innsbruck, Austria
| | - David Erlinge
- />Department of Cardiology, Lund University, Lund, Sweden
| | - Dan Atar
- />Department of Cardiology B, Oslo University Hospital Ullevål and Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Håkan Arheden
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Einar Heiberg
- />Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
- />Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
- />Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, SE-221 85 Lund, Sweden
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Betancur J, Simon A, Halbert E, Tavard F, Carré F, Hernández A, Donal E, Schnell F, Garreau M. Registration of dynamic multiview 2D ultrasound and late gadolinium enhanced images of the heart: Application to hypertrophic cardiomyopathy characterization. Med Image Anal 2016; 28:13-21. [DOI: 10.1016/j.media.2015.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Revised: 10/27/2015] [Accepted: 10/27/2015] [Indexed: 11/25/2022]
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