Machine Learning Techniques for Quantification of Knee Segmentation from MRI

More, Sujeet; Singla, Jimmy; Abugabah, Ahed; AlZubi, Ahmad Ali

doi:10.1155/2020/6613191

Cited by 10 publications

(3 citation statements)

References 76 publications

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“…Using marker-controlled watershed segmentation, which has been used mostly for the detection of brain tumors and knee injuries, has been discussed in many studies. [ 13 14 ] These studies have investigated meniscus tearing and articular surface, but other pathologies such as Baker's cyst have not been investigated yet, so we have decided to evaluate this cyst by using marker-controlled watershed segmentation. The study aims to evaluate the feasibility of the marker-controlled watershed segmentation to detect the Baker's cyst in knee joint's MRI sagittal and axial T2 images.…”

Section: Introductionmentioning

confidence: 99%

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Ghaderi

Ghaznavi

2022

J Med Signals Sens

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Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee's cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of Baker's cyst in the knee's joint MRI sagittal and axial T2-weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker's cyst can be clearly extracted from original images in axial and sagittal planes. The marker-controlled watershed segmentation was able to detect Baker's cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies.

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Section: Introductionmentioning

confidence: 99%

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Ghaderi

Ghaznavi

2022

J Med Signals Sens

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“…However, some small anatomical structures around the joint are hardly detected. For example, the veins and ligaments can show critical early alarms in musculoskeletal lesions [5]. This study will explore the performance of small structure segmentation in knee MRI by using deep learning.…”

Section: Introductionmentioning

confidence: 99%

Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks

et al. 2021

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Accurately identifying the pixels of small organs or lesions from magnetic resonance imaging (MRI) has a critical impact on clinical diagnosis. U-net is the most well-known and commonly used neural network for image segmentation. However, the small anatomical structures in medical images cannot be well recognised by U-net. This paper explores the performance of the U-net architectures in knee MRI segmentation to find a relative structure that can obtain high accuracies for both small and large anatomical structures. To maximise the utilities of U-net architecture, we apply three types of components, residual blocks, squeeze-and-excitation (SE) blocks, and dense blocks, to construct four variants of U-net, namely U-net variants. Among these variants, our experiments show that SE blocks can improve the segmentation accuracies of small labels. We adopt DeepLabv3plus architecture for 3D medical image segmentation by equipping SE blocks based on this discovery. The experimental results show that U-net with SE block achieves higher accuracy in parts of small anatomical structures. In contrast, DeepLabv3plus with SE block performs better on the average dice coefficient of small and large labels.

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“…Using marker-controlled watershed segmentation, which has been used mostly for the detection of brain tumors and knee injuries, has been discussed in many studies [13,14]. These studies have investigated meniscus tearing and articular surface, but other pathologies such as baker's cyst have not been investigated yet, so we have decided to evaluate this cyst by using marker-controlled watershed segmentation.…”

Section: Introductionmentioning

confidence: 99%

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

Ghaderi

Ghaznavi

2021

Preprint

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Introduction: Nowadays, Magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee’s cyst (especially baker) segmentation is one of the novel research areas.Material and Method: There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of baker’s cyst in the knee’s joint MRI sagittal and axial T2-weighted images.Results: The performance of this algorithm was investigated, and the results showed that in a short time baker’s cyst can be clearly extracted from original images in axial and sagittal planes.Conclusion: The marker-controlled watershed segmentation was able to detect baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MR images.

show abstract

Machine Learning Techniques for Quantification of Knee Segmentation from MRI

Cited by 10 publications

References 76 publications

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

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