Cartilage Segmentation Research Articles

Personalised statistical modelling of soft tissue structures in the ankle

Background and objectiveRevealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised ‘in silico’ biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. MethodsA personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an ‘in-house’ generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. ResultsMean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10−17, 1.13 .10−17]) and talus (p = 0.95, CI[ -1.35 .10−17, 1.28 .10−17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area. ConclusionsIn this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.

Entropy and distance maps-guided segmentation of articular cartilage: data from the Osteoarthritis Initiative.

Accurate segmentation of articular cartilage from MR images is crucial for quantitative investigation of pathoanatomical conditions such as osteoarthritis (OA). Recently, deep learning-based methods have made significant progress in hard tissue segmentation. However, it remains a challenge to develop accurate methods for automatic segmentation of articular cartilage. We propose a two-stage method for automatic segmentation of articular cartilage. At the first stage, nnU-Net is employed to get segmentation of both hard tissues and articular cartilage. Based on the initial segmentation, we compute distance maps as well as entropy maps, which encode the uncertainty information about the initial cartilage segmentation. At the second stage, both distance maps and entropy maps are concatenated to the original image. We then crop a sub-volume around the cartilage region based on the initial segmentation, which is used as the input to another nnU-Net for segmentation refinement. We designed and conducted comprehensive experiments on segmenting three different types of articular cartilage from two datasets, i.e., an in-house dataset consisting of 25 hip MR images and a publicly available dataset from Osteoarthritis Initiative (OAI). Our method achieved an average Dice similarity coefficient (DSC) of [Formula: see text] for the combined hip cartilage, [Formula: see text] for the femoral cartilage and [Formula: see text] for the tibial cartilage, respectively. In summary, we developed a new approach for automatic segmentation of articular cartilage from MR images. Comprehensive experiments conducted on segmenting articular cartilage of the knee and hip joints demonstrated the efficacy of the present approach. Our method achieved equivalent or better results than the state-of-the-art methods.

Clinical validation of the use of prototype software for automatic cartilage segmentation to quantify knee cartilage in volunteers

BackgroundThe cartilage segmentation algorithms make it possible to accurately evaluate the morphology and degeneration of cartilage. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence the accuracy of segmentation. It is valuable to evaluate and compare the accuracy and clinical value of volume and mean T2* values generated directly from automatic knee cartilage segmentation with those from manually corrected results using prototype software.MethodThirty-two volunteers were recruited, all of whom underwent right knee magnetic resonance imaging examinations. Morphological images were obtained using a three-dimensional (3D) high-resolution Double-Echo in Steady-State (DESS) sequence, and biochemical images were obtained using a two-dimensional T2* mapping sequence. Cartilage score criteria ranged from 0 to 2 and were obtained using the Whole-Organ Magnetic Resonance Imaging Score (WORMS). The femoral, patellar, and tibial cartilages were automatically segmented and divided into subregions using the post-processing prototype software. Afterwards, all the subregions were carefully checked and manual corrections were done where needed. The dice coefficient correlations for each subregion by the automatic segmentation were calculated.ResultsCartilage volume after applying the manual correction was significantly lower than automatic segmentation (P < 0.05). The percentages of the cartilage volume change for each subregion after manual correction were all smaller than 5%. In all the subregions, the mean T2* relaxation time within manual corrected subregions was significantly lower than in regions after automatic segmentation (P < 0.05). The average time for the automatic segmentation of the whole knee was around 6 min, while the average time for manual correction of the whole knee was around 27 min.ConclusionsAutomatic segmentation of cartilage volume has a high dice coefficient correlation and it can provide accurate quantitative information about cartilage efficiently without individual bias.Advances in knowledge: Magnetic resonance imaging is the most promising method to detect structural changes in cartilage tissue. Unfortunately, due to the structure and morphology of the cartilages obtaining accurate segmentations can be problematic. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence segmentation accuracy. We therefore assessed the factors that influence segmentations error.

AGREEMENT AND SENSITIVITY TO BETWEEN-GROUP DIFFERENCES OF U-NET-BASED, AUTOMATED CARTILAGE T2 ANALYSES - DATA FROM THE OA-BIO PROJECT

Previous studies reported a high agreement for cartilage segmentations from high resolution 3D gradient echo MRIs using deep learning techniques when compared to manual cartilage segmentations with Dice similarity coefficients (DSC) of up to ∼0.90. Only few studies applied deep learning techniques to multi-echo spin echo (MESE) MRI for cartilage T2 analysis and also reported a somewhat lower agreement (DSC up to 0.75). a) to evaluate the agreement between U-Net-based vs. manual cartilage segmentations of the weight-bearing femorotibial joint using MESE MRIs from Osteoarthritis Initiative (OAI) healthy reference cohort (HRC) participants and b) to evaluate, whether U-Net cartilage segmentations allow to reproduce the differences in cartilage T2 times derived from manual segmentation of a previously conducted matched case-control study of Kellgren & Lawrence grade (KLG) 0 knees with vs. without contralateral (CL) joint space narrowing (JSN). Separate U-Nets were trained for the medial (MFTC) and lateral (LFTC) femorotibial compartment using manual quality-controlled cartilage segmentations from sagittal MESE MRIs of 92 OAI HRC participants (training/validation set n=72/10, 50 epochs, weighted cross-entropy loss function). Training was performed either for all 7 echoes or for the 1st echo only to explore whether the exploitation of the information contained in all echoes provides an advantage over using the 1st echo only. The U-Net segmentation was applied to the 10 remaining HRC knees, the 39 KLG 0 knees with CL JSN (CLJSN) and the matched 39 KLG 0 knees with CL KLG 0 (CLKLG0). The agreement between manual and fully-automated U-Net segmentations was evaluated using the DSC and systematic differences in superficial and deep layer cartilage T2. Cross-sectional and longitudinal cartilage T2 differences were compared between CLJSN and CLKLG0 knees for fully-automated and manual segmentations using Cohen's D as measure of effect size. A high agreement with manual segmentations was observed for both the U-Nets trained on all 7 echoes and the U-Nets trained on the 1st echo only (DSC for both: 0.83 to 0.90). The systematic offset was lower for deep layer T2 (-1.5% to +1.7%) than for superficial layer T2 times (0.2% to 5.1%) and was comparable for the U-Nets trained on all 7 echoes and the U-Nets trained on the 1st echo only. Cross-sectionally, all segmentation methods (manual, all-echoes U-Net, 1st-echo U-Net) showed consistently longer T2 in CLJSN knees than in CLKLG0 knees. In the superficial layer, the Cohen's D for the entire femorotibial joint (FTJ) was greatest for the U-Net trained on all echoes (0.62) followed by the U-Net trained on the 1st echo only (0.57) and the manual segmentations (0.53). In the deep layer, the Cohen's D for the entire FTJ was greatest for the U-Net trained on the 1st echo (0.58) followed by the U-Net trained on all echoes (0.47) and the manual segmentations (0.36). Longitudinally, CLJSN knees tended to show a greater increase in deep layer T2 than CLKLG0 knees. The greatest Cohen's D for deep layer T2 was observed for the entire FTJ with the U-Net trained on all echoes (0.59) followed by manual segmentations (0.50) and the U-Net trained on the 1st echo only (0.47). In the superficial layer, all segmentation methods showed similar, small changes for both CLJSN knees and CLKLG0 knees (Cohen's D≤0.26). The U-Nets applied to MESE MRIs showed high agreement with manual femorotibial cartilage segmentations and only small systematic differences. Training the U-Net on all available echoes had no advantage over training the U-Net on the 1st echo only. When applied to KLG 0 knees with CL JSN vs. CL KLG 0, the U-Net segmentation was able to reproduce the differences previously observed for manual segmentations with a comparable effect size. U-Net-based cartilage segmentations from MESE MRIs may therefore be a promising tool for future studies focusing on cartilage T2 analyses. This work was funded as part of the OA-BIO Eurostars-2 project (E! 114932). AW, SM, FE, WW: Chondrometrics GmbH; FWR: Boston Imaging Core Lab IMI-APPROACH (NCT03883568) participants and investigators CORRESPONDENCE ADDRESS: wolfgang.wirth@pmu.ac.at

DO AUTOMATED, U-NET-BASED CARTILAGE SEGMENTATIONS FROM MRI BENEFIT FROM MANUAL QUALITY CONTROL AND CORRECTION? DATA FROM THE IMI-APPROACH PROJECT

Automated, U-Net-based cartilage segmentations have been reported to provide a high sensitivity to cartilage thickness loss. Still, there is no perfect agreement between U-Net and manual segmentations, in particular in OA knees, and outliers with incorrect segmentations may impair the U-Net performance. To evaluate the performance of automated, U-Net-based segmentations of MRIs with and without additional manual quality control and correction (QC&C) and the time effort required for QC&C. 2D U-Nets were trained separately for the medial (MFTC) and the lateral (LFTC) femorotibial compartment of the knee using manual segmentations from 3D MRI acquired by IMI-APPROACH (training/ validation set: n=100/25). The U-Nets were then applied to 26 knees with accelerated MFTC cartilage thickness loss over 2 years and 16 knees without any cartilage loss (age: 67.2±6.8y, 57% females). Subsequently, U-Net-based segmentations were checked and corrected by 5 experienced readers before additional expert quality control. The QC&C required approximately 60% of the time of fully manual segmentations and was reported to be more tedious than performing segmentations from scratch. The agreement (DSC=Dice Similarity Coefficient) of automated vs. manual segmentations before QC&C ranged from 0.83±0.12 to 0.89±0.05 for the 4 femorotibial cartilages and was between 0.89 ± 0.06 and 0.92 ± 0.03 after QC&C. The baseline cartilage thickness was greater for U-Net-based than for manual segmentations in both the MFTC (3.23±0.50mm, 95%CI: [3.07, 3.39] vs. 2.98±0.79mm, 95%CI: [2.73, 3.22]) and LFTC (3.78±0.61mm, 95%CI: [3.59, 3.97] vs. 3.61±0.74mm, 95%CI: [3.38, 3.84]). After QC&C, the observed cartilage thickness (MFTC: 3.00±0.78mm, 95%CI: [2.76, 3.25], LFTC: 3.66±0.7mm, 95%CI: [3.44, 3.88]) was closer to that obtained from manual segmentations. The 2-year cartilage thickness change in the MFTC of knees without cartilage loss was consistent between all 3 methods (manual: 0.01±0.07mm, 95%CI: [-0.03, 0.05], without QC&C: 0.04±0.11mm, 95%CI: [-0.02, 0.10], with QC&C: 0.05±0.12mm, 95%CI: [-0.01, 0.12]). In knees with cartilage loss, the MFTC change tended to be lower before QC&C (-0.22±0.29mm, 95%CI: [-0.34, -0.11]) and with QC&C (-0.22±0.17mm, 95%CI: [-0.28, -0.15]) than for manual segmentations (-0.32±0.17mm, 95%CI: [-0.39, -0.25]). The effect size (Cohen's D) for differences in change between knees with vs. without cartilage loss was greatest for manual segmentations (2.28), followed by U-Net segmentations with QC&C (1.78) and without QC&C (1.13). The sensitivity to change (SRM=standardized response mean) in the MFTC of knees with thickness loss was greatest for manual segmentations (-1.83) followed by the U-Net with QC&C (-1.30) and without QC&C (-0.78). The lower change observed with U-Net-based segmentations may partially be attributed to a regression to the mean effect caused by specifically selecting the knees with the greatest cartilage thickness loss. Still, manual QC&C improved the agreement of automated and manual segmentations and resulted in a greater sensitivity to differences in cartilage thickness loss and also to a greater sensitivity to change when compared to fully-automated U-Net segmentations. This improvement required, however, a substantial amount of time. EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant n° 115770). AW, SM, FE, WW: Chondrometrics GmbH; FWR: Boston Imaging Core Lab; CHL: Merck KGaA (during the conduct of the study) and grants from IMI-APPROACH IMI-APPROACH (NCT03883568) participants and investigators CORRESPONDENCE ADDRESS: xiaosun@arizona.edu

EXPLORING GENDER‐DEPENDENT DIFFERENCES IN PATTERNS OF CARTILAGE LOSS USING RESPONSIVENESS HEAT MAPS: DATA FROM THE OAI

Understanding gender-specific differences in patterns of cartilage loss can improve our knowledge of the pathogenesis of knee osteoarthritis (KOA) development and progression and may inform clinical trials of treatments for KOA. Quantitative cartilage assessment using segmentation software offers an ideal method for appreciating these patterns. The goal of our study was to examine gender differences in patterns of cartilage loss in the central weight-bearing regions of the medial femur (MF), lateral femur (LF), medial tibia (MT), and lateral tibia (LT) using cartilage segmentation software. We measured cartilage volume change in the indexed knee of 480 subjects with Kellgren-Lawrence (KL) 2 and 3 from the OAI at the baseline (BL) and 96-month (mo) time points using the local area cartilage segmentation (LACS) method. Briefly, the LACS method uses robust coordinate systems fixed to anatomical landmarks to find the cartilage volume or thickness in consistent sub-regions on each cartilage plate. (See Figure 1.) Using the coordinate systems, LACS permits assessment of change in much smaller sub-regions through the use of responsiveness heat maps. The average age, BMI, and KL score were 59.5years, 28.7kg/m 2 , 2.03 (males) and 60.5years, 28.8kg/m 2 , 1.99 (females). Figure 2 shows heat map images of the change in cartilage volume from BL to 96mo for males and females. The intensity of the colors (red for loss, blue for gain) represents the standardized response mean (SRM) value at each location. We observed more focused thinning for females in the lateral/posterior portion of the MF and the medial side of the LT. An increase in cartilage thickness in the posterior side of the LF sub-region was also observed for females, as shown with the blue area. Similar patterns were observed for other follow-up time points (i.e., BL to 24mo, BL to 48mo, and BL to 72mo) and when stratifying by age, BMI and KL score. Heat maps of smaller subregions were capable of illustrating gender-specific differences in patterns of cartilage loss, differences that may offer insight into the variation of gender differences in the natural history of KOA. Heat maps of cartilage thickness changes may be useful in evaluating the benefit of interventions for KOA. NIH AR071409 none CORRESPONDENCE ADDRESS: jduryea@bwh.harvard.edu .

Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans.

This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased. A 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan. 77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact). The use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.

Unet Architectures in Multiplanar Volumetric Segmentation - Validated on Three Knee MRI Cohorts

UNet has become the gold standard method for segmenting 2D medical images that any new method must be validated against. However, in recent years, several variations of the seminal UNet have been proposed with promising results. However, there is no clear consensus on the generalisability of these architectures, and UNet currently remains the methodological gold standard. The purpose of this study was to evaluate some of the most promising UNet-inspired architectures for 3D segmentation. For the segmentation of 3D scans, UNet-inspired methods are also dominant, but there is a larger variety across applications. By evaluating the architectures in a different dimensionality, embedded in a different method, and for a different task, we aimed to evaluate if any of these UNet-alternatives are promising as a new gold standard that generalizes even better than UNet. Specifically, we investigated the architectures as the central 2D segmentation core in the Multi-Planar Unet 3D segmentation method that previously demonstrated excellent generalization in the MICCAI Segmentation Decathlon. Generalisability can be demonstrated if a promising UNet-variant consistently outperforms UNet in this setting. For this purpose, we evaluated four architectures for cartilage segmentation from three different cohorts with knee MRIs.

AI MSK clinical applications: cartilage and osteoarthritis.

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.

MRI Knee Domain Translation for Unsupervised Segmentation By CycleGAN (data from Osteoarthritis initiative (OAI)).

Accurate quantification of bone and cartilage features is the key to efficient management of knee osteoarthritis (OA). Bone and cartilage tissues can be accurately segmented from magnetic resonance imaging (MRI) data using supervised Deep Learning (DL) methods. DL training is commonly conducted using large datasets with expert-labeled annotations. DL models perform better if distributions of testing data (target domains) are close to those of training data (source domains). However, in practice, data distributions of images from different MRI scanners and sequences are different and DL models need to re-trained on each dataset separately. We propose a domain adaptation (DA) framework using the CycleGAN model for MRI translation that would aid in unsupervised MRI data segmentation. We have validated our pipeline on five scans from the Osteoarthritis Initiative (OAI) dataset. Using this pipeline, we translated TSE Fat Suppressed MRI sequences to pseudo-DESS images. An improved MaskRCNN (IMaskRCNN) instance segmentation network trained on DESS was used to segment cartilage and femoral head regions in TSE Fat Suppressed sequences. Segmentations of the I-MaskRCNN correlated well with approximated manual segmentation obtained from nearest DESS slices (DICE = 0.76) without the need for retraining. We anticipate this technique will aid in automatic unsupervised assessment of knee MRI using commonly acquired MRI sequences and save experts' time that would otherwise be required for manual segmentation.Clinical relevance- This technique paves the way to automatically convert one MRI sequence to its equivalent as if acquired by a different protocol or different magnet, facilitating robust, hardware-independent automated analysis. For example, routine clinically acquired knee MRI could be converted to high-resolution high-contrast images suitable for automated detection of cartilage defects.

Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping

Segmentation of knee articular cartilage tissue (ACT) from 3D magnetic resonance images (MRIs) is a fundamental task in assessing knee osteoarthritis (KOA). However, automated ACT segmentation of knee cartilage is complicated by (1) the variability of pathological structures in terms of shape, size, spatial resolution and (2) the uncertainties in delineating inter- and intra-cartilage boundaries (possibly touching inter-cartilage surfaces, intensity inhomogeneity, cartilage loss due to disease progression). To address these problems, we propose a novel joint deep and hand-crafted learning-based (JD-HCl) framework. The fully automated segmentation pipeline is a combination of three main steps: 3D convolutional neural networks (CNNs), diffeomorphic mapping, and a conventional hand-crafted feature-based classification model. Firstly, the initial segmentation of knee ACT was drawn from preprocessed knee MRIs using 3D U-Net. Secondly, the spatial alignment in the image domain was carried out using diffeomorphic image registration derived from an estimate of the anatomical correspondences between the subjects and population-specific template. Finally, a tissue-specific hand-crafted feature set was extracted from the spatially aligned pre-segmented ACT region and combined with the semantic context priors obtained from 3D CNN model to develop the second-stage learning model. The proposed method was evaluated with two publicly available Osteoarthritis Initiative (OAI) datasets and one in-house MRI sequences from the Alfred hospital, Melbourne, Australia. The proposed JD-HCl framework produced strong Dice similarity coefficient (DSC), ranging between 97.4% and 98.5% (95% confidence interval) for all cartilage compartments at baseline to 97.2% and 98.5% (the 1-year follow-up) for OAI(-Imorphics) dataset, and 89.3% and 94.6% for OAI-ZIB validation dataset, respectively. Average correlations of cartilage volumes between manual and automatic segmentations were 0.9 and 0.83 for OAI-ZIB dataset, respectively, and 0.975, 0.995, and 0.99 for OAI(-Imorphics) validation datasets, respectively. In longitudinal studies, accurate segmentation of knee ACT using the proposed method produces reproducible cartilage volume, thickness measurements valuable for the study of KOA progression.

Rhinoplasty Pre-Surgery Models by Using Low-Dose Computed Tomography, Magnetic Resonance Imaging, and 3D Printing.

Rhinoplasty and surgical reconstruction of cartilaginous structures still remain a great challenge today. This study aims to identify an imaging strategy in order to merge the information from CT scans and magnetic resonance imaging (MRI) acquisitions and build a 3D printed model true to the patient’s anatomy, for better surgical planning. Using MRI, information can be obtained about the cartilage structures of which the nose is composed. Ten rhinoplasty candidate patients underwent both a low-dose protocol CT scan and a specific MRI for characterization of nasal structures. Bone and soft tissue segmentations were performed in CT, while cartilage segmentations were extrapolated from MRI and validated by both an expert radiologist and surgeon. Subsequently, a 3D model was produced in materials and colors reproducing the density of the three main structures (bone, soft tissue, and cartilage), useful for pre-surgical evaluation. This study has highlighted that the optimization of a CT and MR dedicated protocol has allowed to reduce the CT radiation dose up to 60% compared to standard acquisitions with the same machine, and MR acquisition time of about 20%. Patient-tailored 3D models and pre-surgical planning have reduced the mean operative time by 20 minutes.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

In-Situ Cartilage Functionality Assessment Based on Advanced MRI Techniques and Precise Compartmental Knee Joint Loading through Varus and Valgus Stress.

Stress MRI brings together mechanical loading and MRI in the functional assessment of cartilage and meniscus, yet lacks basic scientific validation. This study assessed the response-to-loading patterns of cartilage and meniscus incurred by standardized compartmental varus and valgus loading of the human knee joint. Eight human cadaveric knee joints underwent imaging by morphologic (i.e., proton density-weighted fat-saturated and 3D water-selective) and quantitative (i.e., T1ρ and T2 mapping) sequences, both unloaded and loaded to 73.5 N, 147.1 N, and 220.6 N of compartmental pressurization. After manual segmentation of cartilage and meniscus, morphometric measures and T2 and T1ρ relaxation times were quantified. CT-based analysis of joint alignment and histologic and biomechanical tissue measures served as references. Under loading, we observed significant decreases in cartilage thickness (p < 0.001 (repeated measures ANOVA)) and T1ρ relaxation times (p = 0.001; medial meniscus, lateral tibia; (Friedman test)), significant increases in T2 relaxation times (p ≤ 0.004; medial femur, lateral tibia; (Friedman test)), and adaptive joint motion. In conclusion, varus and valgus stress MRI induces meaningful changes in cartilage and meniscus secondary to compartmental loading that may be assessed by cartilage morphometric measures as well as T2 and T1ρ mapping as imaging surrogates of tissue functionality.

Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative.

Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm3 ) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845-0.973 and mean differences = 262-501 mm3 for weight-bearing cartilage volume, and r = 0.770-0.962 and mean differences = 0.513-1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers.

Automated analysis of immediate reliability of T2 and T2* relaxation times of hip joint cartilage from 3 T MR examinations

BackgroundMagnetic resonance (MR) T2 and T2* mapping sequences allow in vivo quantification of biochemical characteristics within joint cartilage of relevance to clinical assessment of conditions such as hip osteoarthritis (OA). PurposeTo evaluate an automated immediate reliability analysis of T2 and T2* mapping from MR examinations of hip joint cartilage using a bone and cartilage segmentation pipeline based around focused shape modelling. Study typeTechnical validation. Subjects17 asymptomatic volunteers (M: F 7:10, aged 22–47 years, mass 50–90 kg, height 163-189 cm) underwent unilateral hip joint MR examinations. Automated analysis of cartilage T2 and T2* data immediate reliability was evaluated in 9 subjects (M: F 4: 5) for each sequence. Field strength/sequenceA 3 T MR system with a body matrix flex-coil was used to acquire images with the following sequences: T2 weighted 3D-trueFast Imaging with Steady-State Precession (water excitation; 10.18 ms repetition time (TR); 4.3 ms echo time (TE); Voxel Size (VS): 0.625 × 0.625 × 0.65 mm; 160 mm field of view (FOV); Flip Angle (FA): 30 degrees; Pixel Bandwidth (PB): 140 Hz/pixel); a multi-echo spin echo (MESE) T2 mapping sequence (TR/TE: 2080/18–90 ms (5 echoes); VS: 4 × 0.78 × 0.78 mm; FOV: 200 mm; FA: 180 degrees; PB: 230 Hz/pixel) and a MESE T2* mapping sequence (TR/TE: 873/3.82–19.1 ms (5 echoes); VS: 3 × 0.625 × 0.625 mm; FOV: 160 mm; FA: 25 degrees; PB: 250 Hz/pixel). AssessmentAutomated cartilage segmentation and quantitative analysis provided T2 and T2* data from test-retest MR examinations to assess immediate reliability. Statistical testsCoefficient of variation (CV) and intraclass correlations (ICC2, 1) to analyse automated T2 and T2* mapping reliability focusing on the clinically important superior cartilage regions of the hip joint. ResultsComparisons between test-retest T2 and (T2*) data revealed mean CV's of 3.385% (1.25%), mean ICC2, 1′s of 0.871 (0.984) and median mean differences of −1.139ms (+0.195ms). ConclusionThe T2 and T2* times from automated analyses of hip cartilage from test-retest MR examinations had high (T2) and excellent (T2*) immediate reliability.

A semi-automatic framework based upon quantitative analysis of MR-images for classification of femur cartilage into asymptomatic, early OA, and advanced-OA groups.

To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2-mapping, 2D-WearMap generation, and subregion analysis were performed semi-automatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computer-aided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.

Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation. Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using sixfold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively. On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11s per knee. The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.

Pediatric Cartilage Imaging

To determine if the quantitative MRI techniques T2 and T1ρ mapping are sensitive to ischemic injury to epiphyseal cartilage in vivo in a piglet model of Legg-Calvé-Perthes disease using a clinical 3T MRI scanner. We hypothesized that T2 and T1ρ relaxation times would be increased in the epiphyseal cartilage of operated vs contralateral-control femoral heads 1 week following onset of ischemia.Unilateral femoral head ischemia was surgically induced in eight piglets. Piglets were imaged 1 week post-operatively in vivo at 3T MRI using a magnetization-prepared 3D fast spin echo sequence for T2 and T1ρ mapping and a 3D gradient echo sequence for cartilage segmentation. Ischemia was confirmed in all piglets using gadolinium contrast-enhanced MRI. Median T2 and T1ρ relaxation times were measured in the epiphyseal cartilage of the ischemic and control femoral heads and compared using paired t-tests. Histological assessment was performed on a subset of five piglets.T2 and T1ρ relaxation times were significantly increased in the epiphyseal cartilage of the operated vs control femoral heads (ΔT2 = 11.9 ± 3.7 ms, 95% CI = [8.8, 15.0] ms, P < 0.0001; ΔT1ρ = 12.8 ± 4.1 ms, 95% CI = [9.4, 16.2] ms, P < 0.0001). Histological assessment identified chondronecrosis in the hypertrophic and deep proliferative zones within ischemic epiphyseal cartilage.T2 and T1ρ mapping are sensitive to ischemic injury to the epiphyseal cartilage in vivo at clinical 3T MRI. These techniques may be clinically useful to assess injury and repair to the epiphyseal cartilage to better stage the extent of ischemic damage in Legg-Calvé-Perthes disease.

A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative.

Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age. The pathology of OA disease is still unclear, and there are no effective interventions that can completely alter the OA disease process. Magnetic resonance (MR) image evaluation is sensitive for depicting early changes of knee OA, and therefore important for early clinical intervention for relievingthe symptom. Automated cartilage segmentation based on MR images is a vital step in experimental longitudinal studies to follow-up the patients and prospectively define a new quantitative marker from OA progression. In this paper, we develop a deep learning-based coarse-to-fine approach for automated knee bone, cartilage, and meniscus segmentation with high computational efficiency. The proposed method is evaluated using two-fold cross-validation on 507 MR volumes (81,120 slices) with OA from the Osteoarthritis Initiative (OAI)1 dataset. The mean dice similarity coefficients (DSCs) of femoral bone (FB), tibial bone (TB), femoral cartilage (FC), and tibial cartilage (TC) separately are 99.1%, 98.2%, 90.9%, and 85.8%. The time of segmenting each patient is 12s, which is fast enough to be used in clinical practice. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of OA images.