Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

Awan, Mazhar Javed; Rahim, Mohd Shafry Mohd; Salim, Naomie; Rehman, Amjad; Garcia-Zapirain, Begonya

doi:10.3390/s22041552

Cited by 30 publications

(12 citation statements)

References 59 publications

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“…The ACL tear is a strong band of tissue in the center and an essential part of the knee [ 9 ]. The ACL ligament cannot regenerate; unlike muscle, around 100,000 to 200,000 individuals tear it each year, and 500 million dollars are spent on ACL treatment annually [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan

Rahim

Salim

et al. 2022

Journal of Healthcare Engineering

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In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan

Rahim

Salim

et al. 2022

Journal of Healthcare Engineering

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“…We should especially consider the case when input data 𝒄𝒄 and/or output data 𝒅𝒅 are numeric. Then, instead of matrixes 〈𝑌𝑌|𝐵𝐵 𝑌𝑌 〉 or 〈𝐵𝐵 𝑋𝑋 |𝑋𝑋〉 in (24) we have to apply corresponding operators 𝑩𝑩 𝑋𝑋 and 𝑩𝑩 𝑌𝑌 that transforms L4 data to numerical data or vice versa. Then the forward problem has the form of…”

Section: Solving Inverse Problems Using L4 Matrices By Learning Methodsmentioning

confidence: 99%

Application of the Model of Spots for Inverse Problems

Simonov¹

2022

Preprint

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This article proposes application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using the deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with lack of in-formation about their shapes and are adequate for representation human mental images and reasoning in Artificial Intelligence (AI). However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, basing on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Also, we defined L4 vectors, L4 matrices, and mathematical operations on them. Developed apparatus can be used in AI, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates application of spot-based inverse Radon algorithm for binary image reconstruction.

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“…However, most of these traditional methods have problems such as complex design, poor versatility, and low segmentation accuracy. In recent years, deep learning has been widely used in medical image segmentation [ 11 , 12 , 13 , 14 , 15 , 16 ] and has achieved great success, especially the U-shaped and skip-connection based on convolution (UNet) [ 17 ], because it combines low-resolution information (providing the basis for object category recognition) and high-resolution information (providing the basis for precise segmentation and positioning), which is suitable for medical images segmentation. Then, researchers improved on the basis of UNet and proposed many better medical image segmentation methods [ 18 , 19 , 20 , 21 , 22 , 23 ] such as Att-UNet [ 18 ], Dense-UNet [ 19 ], R2U-Net [ 20 ], UNet++ [ 21 ], AG-Net [ 22 ], and UNet3+ [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation

Chen

Qiu

Yang

et al. 2022

Sensors

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The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore multiscale feature fusion, and there is still much room for improvement. In this paper, we propose a novel multiresolution aggregation transformer UNet (MRA-TUNet) based on multiscale input and coordinate attention for medical image segmentation. It realizes multiresolution aggregation from the following two aspects: (1) On the input side, a multiresolution aggregation module is used to fuse the input image information of different resolutions, which enhances the input features of the network. (2) On the output side, an output feature selection module is used to fuse the output information of different scales to better extract coarse-grained information and fine-grained information. We try to introduce a coordinate attention structure for the first time to further improve the segmentation performance. We compare with state-of-the-art medical image segmentation methods on the automated cardiac diagnosis challenge and the 2018 atrial segmentation challenge. Our method achieved average dice score of 0.911 for right ventricle (RV), 0.890 for myocardium (Myo), 0.961 for left ventricle (LV), and 0.923 for left atrium (LA). The experimental results on two datasets show that our method outperforms eight state-of-the-art medical image segmentation methods in dice score, precision, and recall.

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Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

Cited by 30 publications

References 59 publications

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Application of the Model of Spots for Inverse Problems

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation

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