Accurate Fully Automated 4D Segmentation of Osteoarthritic Knee MRI

Kashyap, Satyananda; Zhang, H.; Sonka, Milan

doi:10.1016/j.joca.2017.02.391

Cited by 4 publications

(5 citation statements)

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“…This process is captured during the human gait. Kashyap et al [48] introduced a 4D fully automated method for knee joint segmentation. The introduced method is extended the 3D LOGISMOS method.…”

Section: Recent Segmentation Approachesmentioning

confidence: 99%

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Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Saba

Rehman

Mehmood

et al. 2018

CMIR

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Knee bone diseases are rare but might be highly destructive. Magnetic resonance imaging (MRI) is the main approach to identify knee cancer and its treatment. Normally, the knee cancers are pointed out with the help of different MRI analysis techniques and latter image analysis strategies understand these images. Computer-based medical image analysis is getting researcher's interest due to its advantages of speed and accuracy as compared to traditional techniques. The focus of current research is MRI-based medical image analysis for knee bone disease detection. Accordingly, several approaches for features extraction and segmentation for knee bone cancer are analyzed and compared on benchmark database. Finally, the current state of the art is investigated and future directions are proposed.

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Section: Recent Segmentation Approachesmentioning

confidence: 99%

“…Lasse et al [47] 2016 Assessment of the impact of patient-particular collagen design on time subordinate. Kashyap et al [48] 2017 A 4D accurate and fully automated segmentation Kashyap et al [49] 2016 LOGISMOS algorithm.…”

Section: Author Year Descriptionmentioning

confidence: 99%

Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Saba

Rehman

Mehmood

et al. 2018

CMIR

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“…M IN-CUT/MAX-FLOW algorithms are ubiquitous in computer vision, since a large variety of computer vision problems can be formulated as min-cut/max-flow problems. Example applications include image segmentation [11,18,49,50,57,84], stereo matching [14,64], surface reconstruction [71], surface fitting [24,60,70,74,97,101], graph matching [48], and texture restoration [88]. In recent years, min-cut/max-flow algorithms have also found use in conjunction with deep learning methods -for example, to quickly generate training labels [61] or in combination with convolutional neural networks (CNNs) [42,80,93].…”

Section: Introductionmentioning

confidence: 99%

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Jensen

Jeppesen

Dahl

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

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“…M IN-CUT/MAX-FLOW algorithms are ubiquitous in computer vision since a large variety of computer vision problems can be formulated as min-cut/max-flow problems. Example applications include image segmentation [9,12,33,34,39,55], stereo matching [10,44], surface reconstruction [47], surface fitting [16,40,46,48,64,67], and texture restoration [58]. In recent years, min-cut/maxflow algorithms have also found use in conjunction with deep learning methods -for example, to quickly generate training labels [41] or in combination with convolutional neural networks (CNNs) [28,53,61].…”

Section: Introductionmentioning

confidence: 99%

“…Performance of parallel algorithms based on build and solve times. The parallel algorithms were run with1,2,4,6,8,12,16,24,32,40,48,56, and 64 threads. Only the best time is shown along with the thread count for that run.…”

mentioning

confidence: 99%

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Jensen¹,

Jeppesen²,

Dahl³

et al. 2022

Preprint

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Minimum cut / maximum flow (min-cut/max-flow) algorithms are used to solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. This makes it difficult to choose an optimal algorithm for a given problem -especially for parallel algorithms, which have not been thoroughly compared. In this paper, we review the state-of-the-art min-cut/max-flow algorithms for unstructured graphs in computer vision. We evaluate run time performance and memory use of various implementations of both serial and parallel algorithms on a set of graph cut problems. Our results show that the Hochbaum pseudoflow algorithm is the fastest serial algorithm closely followed by the Excesses Incremental Breadth First Search algorithm, while the Boykov-Kolmogorov algorithm is the most memory efficient. The best parallel algorithm is the adaptive bottom-up merging approach by Liu and Sun. Additionally, we show significant variations in performance between different implementations the same algorithms highlighting the importance of low-level implementation details. Finally, we note that existing parallel min-cut/max-flow algorithms can significantly outperform serial algorithms on large problems but suffers from added overhead on small to medium problems. Implementations of all algorithms are available at: github.com/patmjen/maxflow algorithms.

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Accurate Fully Automated 4D Segmentation of Osteoarthritic Knee MRI

Cited by 4 publications

References 0 publications

Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

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