A 3D active model framework for segmentation of proximal femur in MR images

Arezoomand, Sadaf; Lee, Won-Sook; Rakhra, Kawan; Beaulé, Paul E.

doi:10.1007/s11548-014-1125-6

Cited by 24 publications

(14 citation statements)

References 31 publications

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“…4 Two previous studies 9,10 reported an accuracy of 1.5 mm when investigating MRI segmentation with another method. Other studies only performed segmentation of the proximal femur 1 or did not report the DSC. This limits the comparison to the results found in the literature.…”

Section: Discussionmentioning

confidence: 99%

“…18 were obtained:(1) an axial-oblique 3D VIBE for the symptomatic unilateral hip joint to provide high-resolution data (repetition time [TR]/ echo time [TE], 15/3.3 milliseconds; flip angles, 4 and 24 ; slice thickness, 0.78 mm; FOV, 160 Â 160 mm; matrix size, 192 Â 192 mm; isotropic voxel size, 0.78 mm 3 , acquisition time [AT], 9 minutes for 128 slices) (Figure 1A); (2) a T1 VIBE Dixon including the entire pelvis (TR/TE1/TE2, 3.94/ 1.27/2.5 milliseconds; flip angle, 9 ; slice thickness, 1 mm; FOV, 312 Â 400 mm; matrix size, 175 Â 320 mm; anisotropic voxel size, 1.2 Â 1.2 Â 1 mm) acquisition time was 32 seconds for 192 slices; and (…”

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confidence: 99%

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Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement

Zeng

Degonda

Boschung

et al. 2021

Orthopaedic Journal of Sports Medicine

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Background: Dynamic 3-dimensional (3D) simulation of hip impingement enables better understanding of complex hip deformities in young adult patients with femoroacetabular impingement (FAI). Deep learning algorithms may improve magnetic resonance imaging (MRI) segmentation. Purpose: (1) To evaluate the accuracy of 3D models created using convolutional neural networks (CNNs) for fully automatic MRI bone segmentation of the hip joint, (2) to correlate hip range of motion (ROM) between manual and automatic segmentation, and (3) to compare location of hip impingement in 3D models created using automatic bone segmentation in patients with FAI. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: The authors retrospectively reviewed 31 hip MRI scans from 26 symptomatic patients (mean age, 27 years) with hip pain due to FAI. All patients had matched computed tomography (CT) and MRI scans of the pelvis and the knee. CT- and MRI-based osseous 3D models of the hip joint of the same patients were compared (MRI: T1 volumetric interpolated breath-hold examination high-resolution sequence; 0.8 mm3 isovoxel). CNNs were used to develop fully automatic bone segmentation of the hip joint, and the 3D models created using this method were compared with manual segmentation of CT- and MRI-based 3D models. Impingement-free ROM and location of hip impingement were calculated using previously validated collision detection software. Results: The difference between the CT- and MRI-based 3D models was <1 mm, and the difference between fully automatic and manual segmentation of MRI-based 3D models was <1 mm. The correlation of automatic and manual MRI-based 3D models was excellent and significant for impingement-free ROM ( r = 0.995; P < .001), flexion ( r = 0.953; P < .001), and internal rotation at 90° of flexion ( r = 0.982; P < .001). The correlation for impingement-free flexion between automatic MRI-based 3D models and CT-based 3D models was 0.953 ( P < .001). The location of impingement was not significantly different between manual and automatic segmentation of MRI-based 3D models, and the location of extra-articular hip impingement was not different between CT- and MRI-based 3D models. Conclusion: CNN can potentially be used in clinical practice to provide rapid and accurate 3D MRI hip joint models for young patients. The created models can be used for simulation of impingement during diagnosis of intra- and extra-articular hip impingement to enable radiation-free and patient-specific surgical planning for hip arthroscopy and open hip preservation surgery.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement

Zeng

Degonda

Boschung

et al. 2021

Orthopaedic Journal of Sports Medicine

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“…Our MPSCL model can progressively refine the pseudo-labels to correct the error and produce better supervision during model training in an 'easy-to-hard' scheme. At the start of model training, since the confidence difference R (1) n between the maximum and submaximum confidence scores for each pixel region is not noticeable, only a few well-adapted pixel regions are selected although the noisy predictions are also removed. But as training proceeds, the generated predictions come gradually closer to the ground-truth labels.…”

Section: G Self-paced Pseudo-labels Visualizationmentioning

confidence: 99%

“…simulations [1]. Recently, some works based on Deep Neural Networks (DNN) have gained impressive advances in medical image segmentation task, e.g., brain lesion [3], neuronal structures [4] and so on.…”

mentioning

confidence: 99%

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

Liu,

Zhu,

Zheng

et al. 2021

Preprint

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To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning. However, such category-agnostic global alignment lacks of exploiting the class-level joint distributions, causing the aligned distribution less discriminative. To address this issue, we propose in this paper a novel margin preserving self-paced contrastive Learning (MP-SCL) model for cross-modal medical image segmentation. Unlike the conventional construction of contrastive pairs in contrastive learning, the domain-adaptive category prototypes are utilized to constitute the positive and negative sample pairs. With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space. To enhance the supervision for contrastive learning, more informative pseudo-labels are generated in target domain in a self-paced way, thus benefiting the category-aware distribution alignment for UDA. Furthermore, the domain-invariant representations are learned through joint contrastive learning between the two domains. Extensive experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance, and outperforms a wide variety of state-of-the-art methods by a large margin.

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“…presented an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs) 29 . CNNs have the unique capability of feature learning,it learn increasingly complex features from data automatically 30 . RNN is a kind of neural network that can predict the future to some extent while being used to analyze time series data (such as analyzing stock prices, predicting buying and selling points).…”

Section: Introductionmentioning

confidence: 99%

Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion

Cai

Wang

et al. 2019

Sci Rep

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Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application.

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A 3D active model framework for segmentation of proximal femur in MR images

Cited by 24 publications

References 31 publications

Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement

Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion

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