Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

퇴행성 슬 관절염의 객관적 평가를 위한 기계학습 기반의 보행 데이터 분석 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2020. 8. 김희찬.Osteoarthritis (OA) is a disease that affects above 30% of the elderly population aged 60 years and older. Western Ontario and McMaster Osteoarthritis (WOMAC) and radiographic-based Kellgren–Lawrence (KL) grade methods are currently used to evaluate the severity of knee osteoarthritis (KOA). However, the WOMAC is a subjective method which cannot be performed to certain patients, and is not suitable for tracking changes in severity over time. KL grade requires highly trained experts and is a time consuming process. This dissertation hypothesized that objective and biomechanical gait data can supplement unmet needs of current gold standard. It was hypothesized that specific features from gait data would reflect the severity of KOA. Therefore, this study aims to identify key gait features associated with the severity of KOA and provide a new objective and explainable evaluation method for KOA based on gait analysis. Features were extracted from the gait signal and an automated severity evaluation model was designed based on machine learning technique for WOMAC severity evaluation model. To develop an automated severity evaluation algorithm for KL grade, features were extracted from the plain radiography image using deep learning network, and machine learning was applied to select features from the gait data. Both image and gait features were used to develop a machine learning algorithm for KL grade evaluation. The evaluation algorithm for WOMAC and KL grade showed a correlation of 0.741 and an accuracy of 75.2% with gold standard method, respectively. This dissertation proposed a new evaluation method for KOA and showed the clinical utility of the gait data application that was limited in clinical practice due to the complexity of the signal.퇴행성 관절염은 60세 이상의 노인 인구 약 30%에서 발병하는 질병이다. 현재 퇴행성 슬 관절염의 진단은 Western Ontario and McMaster Osteoarthritis (WOMAC) 방법과 방사선 촬영 기반의 Kellgren–Lawrence (KL) grade 방법이 사용되고 있다. 그러나 WOMAC 환자의 주관적인 판단을 토대로 중증도를 정량화하는 방법이어서 일부 환자들에게 적용이 불가능하고, 수술 후의 중증도를 반영하지 못한다는 단점이 있다. KL grade은 고도로 훈련된 전문가를 필요로 하며, 정확한 진단을 위하여서는 많은 시간을 필요로 한다. 반면 보행 신호는 환자의 보행에 따른 객관적인 생체 역학 신호를 제공하며, 보행이 가능한 모든 사람에게 적용이 가능하며, 주기적인 추적 관찰에 용의하다. 따라서 본 연구는 보행 신호를 이용하여 객관적이며, 결과에 대한 생체 역학적 이유를 알 수 있는 퇴행성 슬 관절염의 새로운 분석 방법을 제시함에 있다. 먼저 자동으로 WOMAC 방법을 진단하기 위해 보행신호에서 특징들을 추출하고 기계학습 기법을 이용하여 평가하는 모델을 개발하였다. 또한 KL grade 방법을 평가하기 위해 방사선 영상에서 딥러닝 알고리즘으로 추출한 특징들과 보행신호에서 추출한 특징들을 기계학습 기법을 이용하였다. 제안하는 퇴행성 슬 관절염의 평가 방법은 WOMAC 및 KL grade 방법과 각각 상관관계 0.741, 정확도 75.2%를 보였다. 본 연구는 퇴행성 슬 관절염의 새로운 평가 방법을 제시하였으며, 신호의 복잡성으로 인하여 임상에서 사용되지 못했던 보행 신호의 임상적 활용성을 보여주었다.1. Introduction 1 1.1. Knee Osteoarthritis 2 1.2. Severity Evaluation of Knee Osteoarthritis 4 1.2.1. Symptomatic Severity evaluation 4 1.2.2. Structural Severity evaluation 5 1.3. Unmet Clinical Needs 7 1.4. Gait analysis and KOA 8 1.5. Thesis objectives 12 2. Symptomatic Severity of Knee Osteoarthritis 14 2.1. Introduction 15 2.2. Methods 18 2.2.1. Participants 18 2.2.2. Gait Data Collection 20 2.2.3. Statistical Analysis and WOMAC Estimation Model 21 2.3. Results 25 2.4. Discussion 34 2.5. Conclusion 41 3. Structural Severity of Knee Osteoarthritis 42 3.1. Introduction 43 3.2. Methods 49 3.2.1. Participants 49 3.2.2. Gait Data Collection 52 3.2.3. Radiographic Assessment 53 3.2.4. Feature Extraction and Classification 54 3.3. Results 62 3.3.1. Feature Analysis 62 3.3.2. Deep Learning Approach Based on Radiographic Images 72 3.3.3. Proposed Model Based on Gait Data and Radiographic Images 74 3.4. Discussion 76 3.5. Conclusion 83 4. Conclusion 84 4.1. Thesis Summary and Contributions 85 4.2. Future Direction 87 Bibliography 89 Abstract in Korean 98Docto

Quantifying the Tibiofemoral Joint Space Using X-ray Tomosynthesis

Purpose: Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets. Methods: A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior–anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0–5 mm in 0.5 mm increments (VolumeRadTM, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients. Results: The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R2¼0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R2¼0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets. Conclusions: A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee

Automatic knee joint space measurement from plain radiographs

Abstract. Knee osteoarthritis is a common joint disease and one of the leading causes of disability. The disease is characterized by loss of articular cartilage and bone remodeling. Tissue deformations eventually lead to joint space narrowing which can be detected from plain radiographs. Joint space narrowing is typically measured by an experienced radiologist manually, which can be time consuming and error prone process. The aim of this study was to develop and evaluate a fully automatic joint space width measurement method for bilateral knee radiographs. The knee joint was localized from the x-ray images using template matching and the joint space was delineated using active shape model (ASM). Two different automatic joint space measurement methods were tested and the results were validated against manual measurements performed by an experienced researcher. The first joint space width measurements were done by binarizing the joint space and measuring the local thickness of the binary mask using disk fitting. The second method classified bone pixels to tibia and femur. Classification was based on the ASM delineation. Nearest neighbors between femur and tibia were then used to find the joint space width. An automatic method for tibial region of interest (ROI) selection was also implemented. The algorithms used in this thesis were also made publicly available online. The automatically obtained joint space widths were in line with manual measurements. Higher accuracy was obtained using the disk fitting algorithm. Automatic Tibial ROI selection was accurate, although the orientation of the joint was ignored in this study. An open source software with a simple graphical user interface and visualization tools was also developed. Computationally efficient and easily explainable methods were utilized in order to improve accessibility and transparency of computer assisted diagnosis of knee osteoarthritis.Tiivistelmä. Polvinivelrikko on eräs yleisimpiä niveltauteja sekä yksi merkittävimmistä liikuntavammojen aiheuttajista. Nivelrikolle ominaisia piirteitä ovat nivelruston vaurioituminen ja muutokset nivelrustonalaisessa luussa. Kudosten muutokset ja vauriot johtavat lopulta niveltilan kaventumiseen, mikä voidaan havaita röntgenkuvista. Tavallisesti kokenut radiologi tekee niveltilan mittaukset manuaalisesti, mikä vaatii usein paljon aikaa ja on lisäksi virhealtis prosessi. Tämän tutkielman tavoitteena oli kehittää täysin automaattinen niveltilan mittausmenetelmä bilateraalisille polven röntgenkuville. Polvinivel paikallistettiin röntgenkuvista muotoon perustuvalla hahmontunnistuksella ja nivelväli rajattiin käyttämällä aktiivista muodon sovitusta (active shape model, ASM). Nivelvälin mittaukseen käytettiin kahta eri menetelmää, joita verrattiin kokeneen tutkijan tekemiin manuaalisiin mittauksiin. Ensimmäinen nivelvälin mittausmenetelmä sovitti ympyränmuotoisia maskeja niveltilasta tehtyyn binäärimaskiin. Toinen mittausmenetelmä luokitteli luuhun kuuluvat pikselit sääri- ja reisiluuhun. Luokittelu perustui aikaisemmin tehtyyn automaattiseen nivelvälin rajaukseen. Nivelvälin mittaukseen käytettiin lähimpiä naapuripikseleitä sääri- ja reisiluusta. Työssä kehitettiin myös menetelmä automaattiseen sääriluun mielenkiintoalueiden (region of interest, ROI) valintaan. Käytetyt algoritmit ovat julkisesti saatavilla verkossa. Automaattiset nivelväli mittaukset vastasivat manuaalisia mittauksia hyvin. Parempi tarkkuus saatiin käyttämällä ympyrän sovitusta hyödyntävää algoritmia nivelvälin mittaukseen. Sääriluun mielenkiintoalueet onnistuttiin määrittämään automaattisesti, tosin nivelen orientaatiota ei huomioitu tässä työssä. Lisäksi kehitettiin avoimen lähdekoodin ohjelmisto yksinkertaisella graafisella käyttöliittymällä ja visualisointityökaluilla. Työssä käytettiin laskennallisesti tehokkaita ja helposti selitettäviä menetelmiä, mikä edesauttaa tietokoneavusteisen menetelmien käyttöä polvinivelrikon tutkimuksessa

Bone Marrow Lesions and Subchondral Cysts in Association with Severity of Structural Degeneration in Hip Osteoarthritis

This item is only available electronically.Thesis (BHlthMSc(Hons)) -- University of Adelaide, Adelaide Medical School, YEA