GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Zotti, Clément; Luo, Zhiming; Humbert, Olivier; Lalande, Alain; Jodoin, Pierre-Marc

doi:10.1007/978-3-319-75541-0_8

Cited by 54 publications

(45 citation statements)

References 20 publications

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“…The proposed model achieved good Dice accuracy of 0.91 ± 0.04, similar to those reported in the literature (5,(47)(48)(49). This was in spite of facing additional challenges compared to cardiac cine imaging, namely 1) lower spatial resolution, 2) lower and inconsistent SNR and blood-myocardium CNR, and 3) SNR and CNR differences between the control and labeled series.…”

Section: Discussionsupporting

confidence: 85%

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Guo

Yoon

et al. 2019

Magnetic Resonance in Med

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words maximum)Purpose: To apply deep CNN to the segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging and to develop methods that measure uncertainty and that adapt the CNN model to a specific false positive vs. false negative tradeoff. Methods:The Monte Carlo dropout (MCD) U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow (MBF) were available for comparison. We consider two global uncertainty measures, named "Dice Uncertainty" and "MCD Uncertainty", which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific false positive vs. false negative tradeoff. Results:The MCD U-Net achieved Dice coefficient of 0.91 ± 0.04 on the test set. MBF measured using automatic segmentations was highly correlated to that measured using the manual segmentation (R 2 = 0.96). Dice Uncertainty and MCD Uncertainty were in good agreement (R 2 = 0.64). As β increased, the false positive rate systematically decreased and false negative rate systematically increased. Conclusion:We demonstrate the feasibility of deep CNN for automatic segmentation of myocardial ASL, with good accuracy. We also introduce two simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the CNN model to a specific false positive vs. false negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.Keywords: MRI, arterial spin labeling, automatic segmentation, deep convolutional neural network, false positive and false negative tradeoff, uncertainty measure, quality assessment, Bayesian, Monte Carlo Dropout

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Section: Discussionsupporting

confidence: 85%

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Guo

Yoon

et al. 2019

Magnetic Resonance in Med

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“…The networks explored in this work are built on the UNet architecture, which has shown outstanding performance in various medical segmentation tasks . This network consists of a contracting and expanding path, the former collapsing an image down into a set of high‐level features and the latter using these features to construct a pixel‐wise segmentation mask.…”

Section: Methodsmentioning

confidence: 99%

“…The networks explored in this work are built on the UNet architecture, which has shown outstanding performance in various medical segmentation tasks. [41][42][43][44][45] This network consists of a contracting and expanding path, the former collapsing an image down into a set of high-level features and the latter using these features to construct a pixel-wise segmentation mask. The original architecture also proposed skip connections between layers at the same level in both paths, bypassing information from early feature maps to the deeper layers in the network.…”

Section: A Fully Convolutional Neural Networkmentioning

confidence: 99%

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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Purpose Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors’ appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks. Methods The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2‐weighted MRI scans from 60 pathologically confirmed patients with BC. Results Experiments show the proposed model to achieve a higher level of accuracy than state‐of‐the‐art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three‐dimensional (3D) volume, which is between two and three orders of magnitude faster than related state‐of‐the‐art methods for this application. Conclusion We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

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“…To address this problem, a number of works have attempted to introduce additional contextual information to guide 2D FCN. This contextual information can include shape priors learned from labels or multiview images (Zotti et al, 2017(Zotti et al, , 2019Chen et al, 2019b). Others extract spatial information from adjacent slices to assist the segmentation, using recurrent units (RNNs) or multi-slice networks (2.5D networks) (Poudel et al, 2016;Patravali et al, 2017;Du et al, 2019;Zheng et al, 2018).…”

Section: Ventricle Segmentationmentioning

confidence: 99%

“…LGE MR imaging enables the Table 2. Segmentation accuracy of state-of-the-art segmentation methods verified on the cardiac bi-ventricular segmentation challenge (ACDC) dataset All the methods were evaluated on the same test set (50 subjects 2D GridNet-MD with registered shape prior 0.938 0.894 0.910 Khened et al (2019) 2D Dense U-net with inception module 0.941 0.894 0.907 Baumgartner et al (2017) 2D U-net with cross entropy loss 0.937 0.897 0.908 Zotti et al (2017) 2D GridNet with registered shape prior 0.931 0.890 0.912 Jang et al (2017) 2D M-Net with weighted cross entropy loss 0.940 0.885 0.907 Painchaud et al (2019) FCN followed by an AE for shape correction 0.936 0.889 0.909 Wolterink et al (2017c) Multi-input 2D dilated FCN, segmenting paired ED and ES frames simultaneously 0.940 0.885 0.900 Patravali et al (2017) 2D U-net with a Dice loss 0.920 0.890 0.865 Rohé et al (2017) Multi-atlas based method combined with 3D CNN for registration 0.929 0.868 0.881 Tziritas and Grinias (2017) Level-set +markov random field (MRF); Non-deep learning method 0.907 0.798 0.803 Yang et al (2017c) 3D FCN with deep supervision 0.820 N/A 0.780…”

Section: Scar Segmentationmentioning

confidence: 99%

Deep Learning for Cardiac Image Segmentation: A Review

Chen

Qiu

et al. 2020

Front. Cardiovasc. Med.

648

383

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Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

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GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

Cited by 54 publications

References 20 publications

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Deep Learning for Cardiac Image Segmentation: A Review

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