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

Sun, Mengtao; Lü, Li; Hameed, Ibrahim A.; Kulseng, Carl Petter Skaar; Gjesdal, Kjell-Inge

doi:10.3390/app12010283

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…Recent advances in automatic segmentation using convolutional neural networks have reduced the segmentation times of MRI images from several days to a few minutes. For example, Sun et al [33] successfully automatically segmented 12 different structures in a healthy knee in a few minutes.…”

Section: Review a Patient Specific Imagingmentioning

confidence: 99%

“…Analyzing the findings from Section IV-A, we identify MRI as the most relevant modality for a true knee digital twin because of the ability to recreate internal anatomical structures of both soft tissues and bone structures. As shown by Sun et al [33], a healthy knee MRI model can be automatically segmented into 12 robust automatic detection and segmentation of pathologies, such as a partially torn ligament.…”

Section: Patient Specific Imagingmentioning

confidence: 99%

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Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review

et al. 2022

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The use of digital twins to represent a product or process digitally is trending in many engineering disciplines. This term has also been recently introduced in the medical field. In arthroscopic surgery education, the paradigm shift from apprenticeship to simulation training has driven the need for better simulators, and the current focus is on improving simulators with respect to computational efficiency and system accuracy. However, expanding surgical simulations towards digital twins has not yet been explored. This paper introduces the digital twin concept for arthroscopic surgery, and explores its potential in light of the existing scientific literature. Thus, a systematic review was conducted to summarize and analyze the literature with respect to fast and robust design of an arthroscopic digital twin using patientspecific information, and methods for interactive surgical soft tissue simulation. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol with three reliable scientific search engines: IEEE Explore, ScienceDirect and PubMed. Eighty papers were included in the review, and the extracted data included modeling methods, tissue types, constitutive behavior, computational efficiency or accuracy, hardware configuration, haptic device description, software tools, and system architectures. Considering the review, a novel macro-level conceptual arthroscopic digital twin system is presented, and the applicability of the review findings for the identified subsystems are discussed. The proposed system integrates patient-specific images, diagnostic data, intraoperative sensor data, and surgical practice as inputs, and conceptually enables surgical skills training, preoperative planning, and a database of virtual surgeries.

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Section: Review a Patient Specific Imagingmentioning

confidence: 99%

Section: Patient Specific Imagingmentioning

confidence: 99%

Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review

et al. 2022

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“…The deformed mesh y mesh and tool position y tool are sent from the control software through a serial port. A generic anatomic knee model obtained from magnetic resonance imaging scans and segmented using the method described in [35], was imported. A CAD-model of the Acufex arthroscopic punch was developed and imported.…”

Section: Haptic Arthroscopic Punch a System Setupmentioning

confidence: 99%

Intraoperative Data-Based Haptic Feedback for Arthroscopic Partial Meniscectomy Punch Simulation

et al. 2022

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The use of intraoperative tool-tissue interaction data for arthroscopic digital twins has the potential to enhance haptic feedback in surgical simulations. This study demonstrates the implementation of an interpolation-based method, and a Kalman filter-based method using intraoperative interaction data to provide nonlinear haptic feedback in a partial meniscectomy punch simulation. These methods were demonstrated using a novel inexpensive haptic system for simulating an arthroscopic partial meniscectomy punch. Tool-tissue interaction data were collected from a cadaveric meniscus and used for the simulations. The software, computer-aided design models, and collected data are provided open-source on GitHub. The face validity of the methods was evaluated in user studies, including six experienced orthopedic surgeons, and five non-professionals. The findings showed that experienced surgeons could distinguish between a nonlinear interpolation-based force signal, and a linear-elastic ideally-plastic finite element-based reference signal in a partial meniscectomy punch simulation. Surgeons preferred the interpolation method as a haptic rendering strategy in this study. The Kalman filter method was not as effective as interpolation in recreating nonlinear haptic feedback for the tested parameters, but demonstrated coupling of haptic force signals from intraoperative data and an idealized finite element method in a partial meniscectomy punch simulation.

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“…Recently many deep learning approaches have been proposed to solve the problem of segmentation through radiology images in the medical field. The U-Net [26] is a convolutional neural network that was initially designed for semantic segmentation for medical images [27,28].…”

Section: Introductionmentioning

confidence: 99%

Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

Awan

Rahim

Salim

et al. 2022

Sensors

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The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.

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

Cited by 7 publications

References 42 publications

Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review

Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review

Intraoperative Data-Based Haptic Feedback for Arthroscopic Partial Meniscectomy Punch Simulation

Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

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