Visual Localisation for Knee Arthroscopy

Banach, Artur; Strydom, Mario; Jaiprakash, Anjali; Carneiro, Gustavo; Eriksson, Anders; Crawford, Ross; McFadyen, Aaron

doi:10.1007/s11548-021-02444-8

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Since the visual field in arthroscopic surgery is often affected by different angles of the joint, which requires repositioning in different visual fields, automatic localization under the arthroscopic field of view is urgently needed. Recently, Banach A et al collected arthroscopic videos and performed deep learning on video sequences at four different knee angles, and the results showed that deep learning performed well in arthroscopic field of view ( 46 ). Besides, Sarin JK et al argued that deep learning can detect joint degeneration and help determine the boundaries between diseased cartilage and normal cartilage in arthroscopic surgery ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

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Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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“…The setup allows the surgeon to inspect the joint through the arthroscopic view displayed on a screen while altering the leg position to allow the scope to capture the required tissues. Despite offering several clinical advantages, arthroscopic procedures face long-standing challenges such as physicallydemanding ergonomics during patient manipulation, lack of depth perception, limited field of view, and counter-intuitive hand-eye coordination between scope and surgical instruments.Even with skilled operator manipulation, a complex procedure like this can cause unintended trauma and post-operative complications [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy

et al. 2022

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Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34∘ and an average registration error of 3.45 mm and 7.76∘ is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77∘ is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.

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Visual control for robotic 3D printing on a moving platform

Chaudhry,

Czekanski

2024

Mechatronics

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Visual Localisation for Knee Arthroscopy

Cited by 3 publications

References 28 publications

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy

Visual control for robotic 3D printing on a moving platform

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