MRI Atlas of Orthopedics and Traumatology of the Knee

Teller, Peter; König, H; Weber, Ulrich; Hertel, P.

doi:10.1007/978-3-642-55620-3

Cited by 3 publications

(2 citation statements)

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“…Most likely the adhesions arose secondary to inflammation [20] connected with the nail insertion and/or nail removal. The low signal changes in Hoffa's fat pad seemed to be as a result of extensive oedema [21] and are also most likely post-operative in nature. Subcutaneous low signal nodes in front of the patellar ligament and a low signal lid in the nail insertion hole were found in all of the patients.…”

Section: Discussionmentioning

confidence: 93%

MRI of the knee after locked unreamed intramedullary nailing of tibia

Gustafsson¹,

Jonsson

2008

Chir Organi Mov

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We analysed the reason for knee pain after intramedullary (IM) nailing with standard MRI sequences at a mean of 27 months (range 2-45) after nail removal in eleven patients with a mean age of 30 years (range 15-52). Knee pain was assessed in four grades. All our patients had signal changes of fluid in the nail channel, areas of low signal intensity in Hoffa's fat pad and subcutaneous low signal nodes in front of a thickened patellar ligament. Ten patients had low signal adhesions from the nail insertion towards the patellar ligament. Six patients had severe knee pain and even marked adhesions. Two patients had no knee pain; one of these two had minimal adhesions and the other one no adhesions. Five of the patients had a meniscal tear and/or local cartilage reduction. The degree of adhesions from the nail insertion in the tibia towards the patellar ligament was proportional to the degree of knee pain. No sign of acute inflammation was found.

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Section: Discussionmentioning

confidence: 93%

MRI of the knee after locked unreamed intramedullary nailing of tibia

Gustafsson¹,

Jonsson

2008

Chir Organi Mov

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“…Magnetic resonance images have a great role in diagnosis, treatment and improvement of joint disease such as arthritis. Arthritis consists of progressive degeneration (figure 2) of both the bone and soft tissues [8]. Deep learning has many models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-term memory Networks (LSTM), AutoEncoders and Generative Adversarial Networks (GAN) [9].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Knee MRI Cancellous bone Classification on Google Colaboratory using Convolutional Neural Network

Awan¹

2019

IJATCSE

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The field of Biomechanical engineering and Orth ology is related to knee bone tissue in which cancellous bone lies. The cancellous bone also called spongy bones has greater surface area for this it causes Osteoporosis (OP), Osteoarthritis (OA), and knee cartilage and knee replacement. The knee bone images are measured mostly by Magnetic Resonance Imaging (MRI).In this paper we presented deep learning model on cancellous bones (tiff type) MRI through Convolutional Neural Network (CNN) to predict the image classification which achieved 99.39 % accuracy. The sample size of images are 185 cancellous MRI and 185 tiff images. Further we trained our model on cloud service that is Google Colabaratory (Colab) which is Graphical Processing Unit (GPU). The accuracy of this model is same but the execution time per min decreases on GPU environment. We increased the no of epochs 20 then 50 its execution time is 10 times less than CPU. The execution time on GPU google Colab is 2.23 (mins) and on CPU its 24.23(mins).

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Automated segmentation of trabecular and cortical bone from proton density weighted MRI of the knee

Chen

Sprengers

Kang

2018

Med Biol Eng Comput

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Patient-specific implant design and pre- and intra-operative planning is becoming increasingly important in the orthopaedic field. For clinical feasibility of these techniques, fast and accurate segmentation of bone structures from MRI is essential. However, manual segmentation is time intensive and subject to inter- and intra-observer variation. The challenge in developing automatic segmentation algorithms for MRI data mainly exists in the inhomogeneity problem and the low contrast among cortical bone and adjacent tissues. In this paper, we proposed a method for automatic segmentation of knee bone structures for MRI data with a 3D local intensity clustering-based level set and a novel approach to determine the cortical boundary utilizing the normal vector of the trabecular surface. Application to clinical imaging data shows that our method is robust to MRI inhomogeneity. In comparing our method to manual segmentation in 18 femurs and tibiae, we found a dice similarity coefficient (DSC) of 0.9611 ± 0.0052 for the femurs and 0.9591 ± 0.0173 for tibiae. The average surface distance error was 0.4649 ± 0.1430 mm for the femurs and 0.4712 ± 0.2113 mm for the tibiae. The results of the automatic technique thus strongly corresponded to the manual segmentation using less than 3% of the time and with virtually no workload. Graphical abstract ᅟ

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MRI Atlas of Orthopedics and Traumatology of the Knee

Cited by 3 publications

References 0 publications

MRI of the knee after locked unreamed intramedullary nailing of tibia

MRI of the knee after locked unreamed intramedullary nailing of tibia

Acceleration of Knee MRI Cancellous bone Classification on Google Colaboratory using Convolutional Neural Network

Automated segmentation of trabecular and cortical bone from proton density weighted MRI of the knee

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