Deep 3D Convolutional Networks to Segment Bones Affected by Severe Osteoarthritis in CT Scans for PSI-Based Knee Surgical Planning

Marzorati, D.; Sarti, Mattia; Mainardi, Luca; Manzotti, Alfonso; Cerveri, Pietro

doi:10.1109/access.2020.3034418

Cited by 13 publications

(19 citation statements)

References 53 publications

(57 reference statements)

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“…Increasing complexity of the model might enhance the performance, but it will cause the training of the model to be computationally heavy and the cost of computation to be expensive. Although 3D CNN is more computationally challenging [ 64 ], 3D CNN models show similar accuracy to the experts in computer-aided diagnosis performance [ 68 ]. Hence, future research can investigate the optimization of 3D CNNs by reducing the architecture's complexity and the training parameters.…”

Section: Discussionmentioning

confidence: 99%

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Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

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Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.

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Section: Discussionmentioning

confidence: 99%

“…Marzorati et al [ 64 ] performed automatic femur and tibia segmentation from CT images to extract pathological OA features. Results presented that implementation of 3D-U-Net in bone segmentation outperformed 2D-U-Net despite having more processing layers.…”

Section: Application Of 3d Deep Learning In Knee Osteoarthritis Assessmentmentioning

confidence: 99%

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

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“…Concurrently with the emergence of MR technologies, there have been substantial advances in artificial intelligence (AI) tools, such as convolutional neural networks (CNN), that showcased the potential of greatly automatizing image processing in many different clinical applications, for diagnostic and surgical planning purposes [13] . In orthopedics, CNN models were applied for the segmentation of knee bones and cartilage from magnetic resonance imaging providing invaluable support in the diagnosis of osteoarthritis [14] , [15] , for the automatic segmentation of bones in knee CT scans for the realization of personalized cutting guides [7] , [16] , [17] , for total knee arthroplasty planning using X-ray radiographs [18] , [19] , [20] . Some initial studies have explored the potential of integrating AI tools with technologies for extended reality in hip and knee arthroplasty [21] .…”

Section: Introductionmentioning

confidence: 99%

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Moglia,

Marsilio,

Rossi

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p$\end{document} = 0.14 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p$\end{document} = 0.13, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p < 0.05$\end{document} ). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

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“…Concerning the knee joint, our deep learning-based model shows better results than graph-cuts and shape prior combination reported in [5], or than region-based active contour segmentation used by the authors of [6]. In [7], authors used 3D U-net to segment the proximal tibia and distal femur and reached Dice scores comparable to ours.…”

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confidence: 59%

Tibial and femoral bones segmentation on CT-scans: a deep learning approach

Maintier¹,

Maguet²,

Clavé³

et al.

EPiC Series in Health Sciences

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Custom implants in Total Knee Arthroplasty (TKA) could improve prosthesis’ durability and patient’s comfort, but designing such personalized implants requires a simplified and thus automatic workflow to be easily integrated in the clinical routine. A good knowledge of the shape of the patient's femur and tibia is necessary to design it, but segmentation is still today a key issue. We present here an automatic segmentation approach of the three joints of the lower limb: hip, knee and ankle, using convolutional neural networks (CNNs) on successive transverse views from CT images. Our three 2D CNNs are built on the U-net model, and their specialization each on one joint allowed us to achieve promising results presented here. This could be integrated in a TKA planning software allowing the automatic design of TKA custom implants.

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Deep 3D Convolutional Networks to Segment Bones Affected by Severe Osteoarthritis in CT Scans for PSI-Based Knee Surgical Planning

Abstract: We would like to thank Medacta International SA for providing patient data.

Cited by 13 publications

References 53 publications

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Tibial and femoral bones segmentation on CT-scans: a deep learning approach

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