Review of automated segmentation approaches for knee images

Ridhma,; Kaur, Manvjeet; Sofat, Sanjeev; Chouhan, Devendra Kumar

doi:10.1049/ipr2.12045

Cited by 16 publications

(12 citation statements)

References 98 publications

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“…However, the adoption of a heavier model often necessitates a smaller image size due to the increase in the number of parameters. This predicament is particularly pronounced when segmenting knee anatomy, a task complicated by factors such as delicate cartilage, intensity variations, and irregular shapes [4]. To address this challenge and allow the use of larger image patches, it is highly desirable to minimize redundant features in network design.…”

Section: Introductionmentioning

confidence: 99%

3D spatial and channel reconstruction for large image patch based segmentation of knee anatomical structures on MRI

Yu,

Zhao,

Singh

et al. 2024

Medical Imaging 2024: Image Processing

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The complexity of three-dimensional (3D) image segmentation exceeds that of its 2D counterpart, primarily due to increased memory demands resulting from the larger size of input images. To mitigate these challenges, a common strategy is to extract patches from the original preprocessed images. The use of sophisticated models can sometimes offset the trade-off associated with input image size, but increased image sizes tend to yield superior performance. In this study, we aim to alleviate memory constraints while improving the accuracy of segmenting 9 distinct substructures within the knee anatomy as compared to state-of-the-art models. Approach: We utilized a cohort of 183 knee MRI scans, with nine anatomical structures annotated by radiologists. This dataset was divided into training, validation, and independent testing sets at a ratio of 8:1:1, respectively. The second standard convolutional layer of the nnU-Net is substituted with our MS-SCConv module at each down-sampling stage. The MS-SCConv module employs soft attention mechanisms to weight both the original and refined feature maps. The refined feature maps are produced using the SCConv process, which integrates two units specifically designed to minimize feature redundancy from both spatial and channel perspectives. Comprehensive ablation studies were performed to evaluate the specific contributions of the MS-SCConv module within the informative nnU-Net framework. The performance of our proposed model was compared with several other models, including U-Net, UNETR, DynU-Net and nnU-Net. Results: Informative nnU-Net demonstrated superior segmentation performance, achieving the high Dice coefficient (0.83) compared to other models, namely U-Net (0.71), UNETR (0.71), DynU-Net (0.74), and nnU-Net (0.81) (p<0.05). Notably, Informative nnU-Net achieves improved accuracy while reducing the number of parameters from 27.2M to 26.9M. Conclusions: Our experiments revealed that with MS-SCConv and informative nnU-Net, the number of parameters can be decreased while improving segmentation performance.

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Section: Introductionmentioning

confidence: 99%

3D spatial and channel reconstruction for large image patch based segmentation of knee anatomical structures on MRI

Yu,

Zhao,

Singh

et al. 2024

Medical Imaging 2024: Image Processing

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show abstract

“…Although radiography is the primary imaging tool for knee OA diagnosis, it lacks sensitivity to subtle changes in soft tissue structures, which often occur in the early stage of this disease [7,9,10]. Magnetic resonance imaging (MRI) is commonly used for non-invasive assessment of the knee joint structure because it provides excellent soft tissue contrast [11,12].…”

Section: Introductionmentioning

confidence: 99%

Source-free unsupervised adaptive segmentation for knee joint MRI

Li,

Zhao,

Zhang

et al. 2024

Biomedical Signal Processing and Control

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“…Obtaining precise and robust automatic segmentation from MR images is challenging [10,12]. Compared with cartilage segmentation, bone segmentation is easier because the knee bone has a regular shape and a large anatomical size [13].…”

Section: Introductionmentioning

confidence: 99%

Source-free Unsupervised Adaptive Segmentation for Knee Joint MRI

Li¹,

Zhao²,

Hong³

et al. 2023

Preprint

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<p>Knee osteoarthritis is a prevalent disease worldwide. The automatic segmentation of knee tissues in magnetic resonance (MR) images has important clinical utility in assessing knee osteoarthritis. Deep learning- based methods show great potential in this application, but they often require a large amount of labeled training data, which is challenging and expensive to acquire. Unsupervised domain adaptation that transfers the learned knowledge from a source labeled dataset to a target unlabeled dataset can be used to address this problem. However, in medical scenarios, domain adaption techniques are often limited by access to the source data due to concerns about patient privacy. In this work, we proposed a novel and effective source-free unsupervised domain adaptation method for knee joint multi-tissue segmentation that does not require a source dataset. The proposed framework is split into two stages. In the first stage, matching batch normalization statistics guides the first segmentation network to realize model adaptation and is combined with augmented entropy minimization to obtain pseudo segmentation labels for the target MR images. The pseudo labels generated by the first segmentation network are then refined using a voting strategy to supervise the training of the models in the second stage. In the second stage, uncertainty-aware cross pseudo supervision is used to further boost the performance of the desired segmentation network, which comprises an encoder and a primary decoder. Our experiments demonstrated that the proposed method outperforms the current state-of-the-art source-free unsupervised domain adaptation techniques for segmenting knee tissues in MR images. Our study also demonstrated that the proposed approach is effective in reverse-directional adaptation. </p>

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Review of automated segmentation approaches for knee images

Cited by 16 publications

References 98 publications

3D spatial and channel reconstruction for large image patch based segmentation of knee anatomical structures on MRI

3D spatial and channel reconstruction for large image patch based segmentation of knee anatomical structures on MRI

Source-free unsupervised adaptive segmentation for knee joint MRI

Source-free Unsupervised Adaptive Segmentation for Knee Joint MRI

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