240 research outputs found

    Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

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    Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams

    Deep Learning for Musculoskeletal Image Analysis

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    The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.Comment: Invited Paper, ASILOMAR 2019, TP4b: Machine Learning Advances in Computational Imagin

    Machine Learning Techniques for Quantification of Knee Segmentation from MRI

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    © 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed

    Structural features of early-stage OA:Keep your menisci in good shape

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    Structural features of early-stage OA:Keep your menisci in good shape

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    Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details

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    This paper presents MRPyrNet, a new convolutional neural network (CNN) architecture that improves the capabilities of CNN-based pipelines for knee injury detection via magnetic resonance imaging (MRI). Existing works showed that anomalies are localized in small-sized knee regions that appear in particular areas of MRI scans. Based on such facts, MRPyrNet exploits a Feature Pyramid Network to enhance small appearing features and Pyramidal Detail Pooling to capture such relevant information in a robust way. Experimental results on two publicly available datasets demonstrate that MRPyrNet improves the ACL tear and meniscal tear diagnosis capabilities of two state-of-the-art methodologies. Code is available at https://git.io/JtMPH

    Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification

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    Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.Comment: Junru Zhong and Yongcheng Yao share the same contribution. 17 pages, 4 figures, 4 table

    Image processing in detection of knee joints injuries based on MRI images

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    This paper presents image processing methods for visualization and classification of medial meniscus tears. The first method uses watershed with a threshold segmentation approach. The algorithm was tested on a number of images of the knee obtained with a use of the magnetic resonance imaging technique (MR). Images of the knee were collected from healthy subjects and patients with a clinically diagnosed meniscal pathology. Then, watershed technique was compared with other popular methods of image segmentation, i.e. simple thresholding and region growing. For this purpose, the execution speed and the efficiency of the methods were analyzed. Additionally, an automatic detection of the meniscus based on MRI of the knee joint was developed. The solutions were implemented using classical image processing methods in the MATLAB environment with an application of the Image Processing Toolbox and MVtec Halcon vision libraries. The presented methods will have a practical value for the referring physicians and the diagnostic imaging specialists

    Ex vivo analysis of local orientation of collagen fiber bundles in 3D in posterior horn human meniscus using micro-computed tomography

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    Abstract. Objective: Knee osteoarthritis (OA) is an increasingly relevant joint disease affecting mostly aged population in developed countries. However, there is currently no treatment for OA and increasing the knowledge of the disease with the help of micro-computed tomography (µCT) imaging could offer help in finding the solution. The objective of this thesis was to quantitatively analyze the microstructural organization of human posterior horn meniscus samples in 3D using hexamethyldisilazane (HMDS) based µCT imaging. In addition, this study aims to compare the local microstructural organization of meniscus between OA patients and healthy references. Method: We collected medial and lateral posterior horns of human menisci from 10 endstage medial compartment knee OA patients undergoing total knee replacement surgery and from 10 deceased donors without diagnosed knee OA to act as healthy reference. The posterior horns were dissected and fixed in formalin, dehydrated in ascending alcohol concentrations, treated with HMDS, and scanned with a desktop µCT. Furthermore, we performed local orientation analysis of collagenous microstructure in 3D to all samples, by calculating local structure tensors from greyscale gradients withing a selected integration window to determine the polar angle for each voxel. Moreover, distribution of angles and mean estimated average angles were statistically compared. Results: Collagen fiber bundles in HMDS-treated meniscal samples were depicted in 3D using µCT. In the quantitative local orientation analysis, medial OA had overall lowest orientation angles compared to all other groups: mean estimated differences versus medial OA were -24° [95%CI -31°, -18°] in medial donor, -25° [95%CI -34°, -15°] in lateral OA, and -25° [95%CI -35°, -16°] in lateral donor groups. Distribution and mean angles between lateral OA and lateral donor menisci were similar with a mean difference of 2°. Conclusions: In this study, we were able to quantitatively analyze collagen fiber bundles and their orientations in 3D in the posterior horn of human meniscus using HMDS-based µCT imaging. Furthermore, collagen disorganization increased in the medial OA meniscus, suggesting a relationship between collagenous microstructure disorganization and meniscus degradation.Ihmisen nivelkierukan takasarven kollageenisäiekimppujen kolmi-ulotteinen lokaaliorientaatioanalyysi mikrotietokonetomografian avulla. Tiivistelmä. Tarkoitus: Nivelrikko on yleinen sairaus vanhenevassa yhteiskunnassa, mutta sairauden monimuotoisuuden vuoksi sen hoitaminen on vaikeaa. Sairauden vahvempi ymmärtäminen voisi auttaa hoidon kehittämisessä sairautta vastaan. Tämän pro gradu -tutkielman tavoitteena oli analysoida kvantitatiivisesti ihmisen nivelkierukkanäytteiden mikrorakenteiden orientaatiota kolmiulotteisesti käyttäen heksametyylidisilatsaaniin (HMDS) perustuvaa näytteenkäsittelytekniikkaa mikrotietokonetomografiakuvantamisessa (µCT). Lisäksi tässä työssä verrataan kierukan lokaalimikrorakenneorganisaatiota nivelrikkoisten potilaiden ja terveiden verrokkien välillä. Menetelmä: Keräsimme mediaali- ja lateraalipuolen nivelkierukan takasarvet kymmeneltä nivelrikon loppuvaiheen potilaalta, joille tehtiin polven tekonivelleikkaus, ja 10 menehtyneeltä oikeuslääketieteen potilaalta, joilla ei ollut diagnosoitua polven nivelrikkoa. Nivelkierukoiden posterioriset sarvet leikattiin, käsiteltiin formaliinilla, kuivattiin nousevissa etanolipitoisuuksissa, käsiteltiin HMDS:llä ja kuvattiin µCT-laitteella. Lisäksi teimme näytteille kolmiulotteisen orientaatioanalyysin, jolla mitataan näytteiden kollageenisen mikrorakenteen orientaatiota. Analyysi laskee µCT-kuvien harmaasävygradienttien avulla jokaiselle vokselille paikallisen rakennetensorin, joiden purkamisesta saadaan laskettua vokselin anisotropian määrä ja sen pienimmän vektorin suunta. Pienin arvo ja sen suunta voidaan määrittää vokselin pääasialliseksi orientaatioksi. Kulmien jakautumista ja keskimääräisiä kulmia verrattiin tilastollisesti terveiden ja nivelrikkopotilaiden mediaali- ja lateraalipuolten välillä. Tulokset: Nivelkierukan kollageenikimput kuvattiin kolmiulotteisesti µCT:llä käyttäen HMDS-käsiteltyjä nivelkierukkanäytteitä. Kvantitatiivisessa orientaatioanalyysissä todettiin mediaalipuolen nivelrikkoisilla nivelkierukoilla yleisesti enemmän disorganisaatiota kaikkiin muihin ryhmiin verrattuna: mediaalipuolen nivelrikkoryhmässä orientaatioiden ero verrattuna mediaaliverrokkiryhmään oli -24° [95%CI -31°, -18°], -25° [95%CI -34°, -15°] verrattuna lateraalipuolen nivelrikkoryhmään ja -25° [95%CI -35°, -16°] verrattuna lateraalipuolen luovuttajaryhmän välillä. Lisäksi lateraalipuolen luovuttaja- ja lateraalipuolen nivelrikkoryhmän välillä kulmien jakauma ja keskiarvo olivat samanlaiset keskimäärisen eron ollessa 2°. Johtopäätökset: Tässä tutkimuksessa onnistuimme kuvaamaan ihmisen nivelkierukan kollageenikimput sekä kvantitatiivisesti analysoimaan niiden kolmiulotteista orientaatiota käyttäen HMDS-pohjaista µCT-kuvantamista. Lisäksi kollageenin disorganisaatio oli suurin mediaalipuolen nivelrikkoisessa nivelkierukassa, mikä viittaa kollageenisen mikrorakenteen disorganisaatioon ja nivelkierukan degeneraation väliseen vahvaan suhteeseen

    Computer aided analysis of inflammatory muscle disease using magnetic resonance imaging

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    Inflammatory muscle disease (myositis) is characterised by inflammation and a gradual increase in muscle weakness. Diagnosis typically requires a range of clinical tests, including magnetic resonance imaging of the thigh muscles to assess the disease severity. In the past, this has been measured by manually counting the number of muscles affected. In this work, a computer-aided analysis of inflammatory muscle disease is presented to help doctors diagnose and monitor the disease. Methods to quantify the level of oedema and fat infiltration from magnetic resonance scans are proposed and the disease quantities determined are shown to have positive correlation against expert medical opinion. The methods have been designed and tested on a database of clinically acquired T1 and STIR sequences, and are proven to be robust despite suboptimal image quality. General background information is first introduced, giving an overview of the medical, technical, and theoretical topics necessary to understand the problem domain. Next, a detailed introduction to the physics of magnetic resonance imaging is given. A review of important literature from similar and related domains is presented, with valuable insights that are utilised at a later stage. Scans are carefully pre-processed to bring all slices in to a common frame of reference and the methods to quantify the level of oedema and fat infiltration are defined and shown to have good positive correlation with expert medical opinion. A number of validation tests are performed with re-scanned subjects to indicate the level of repeatability. The disease quantities, together with statistical features from the T1-STIR joint histogram, are used for automatic classification of the disease severity. Automatic classification is shown to be successful on out of sample data for both the oedema and fat infiltration problems
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