Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels

PROCESS VALIDATION IN CALCULATING MEDIAN PROXIMITY IN TIBIOFEMORAL CARTILAGE DEFORMATION UNDER FULL BODY LOADING

INTRODUCTION Knee osteoarthritis (OA) is characterized by progressive and irreversible degradation of tibiofemoral (TF) cartilages. Anterior cruciate ligament (ACL) rupture is a known risk factor for post-traumatic OA (PTOA) [1]. However, there are currently no in-vivo tests to diagnose pre-radiographic PTOA. Following injury, the cartilage macromolecular matrix weakens, cartilage swells and consequently cartilage softness increases [2]. This research investigates the in-vivo effects of ACL injury on cartilage deformation magnitude and rate under full body loading. The objective of this project was to determine the consequences of cartilage model mesh types and incremental mesh simplifications on the accuracy of resultant TF cartilage proximities. METHODS The affected knee of a 37 year old male PTOA subject (ACL deficient for 6 years) was imaged using Magnetic Resonance Imaging (FIESTA sequence; 3T GE Discovery 750). 3D TF bone and cartilage models were generated in Amira (VSG, Germany). The subject performed a 10 minute standing task in the Dual Fluoroscopic (DF) laboratory. DF images (32LP/mm) were collected at 6Hz. Bone alignments were reconstructed from DF images using AutoScoper (Brown University, USA) and cartilage models were co-registered. TF cartilage surface proximity was determined as the surface normal distance from each triangular mesh face onto the opposing cartilage. (Matlab, v2014b, The MathWorks, USA). The effects on surface proximities of three types of triangular cartilage surface meshes, generated in Amira, were analysed: 1) Basic Simplification - reducing face numbers with variable mesh size; 2) Remeshed Surface – isotropic mesh; 3) Iteratively Smoothed Remeshed Surface. Face numbers were reduced at 10% increments from the original surface for each surface type. RESULTS Median proximity errors for the Remeshed Surface were consistently smaller than the other mesh types across all four cartilage surface compartments. The medial tibial plateau displayed a rapid increase in error (Figure 1) indicating a high sensitivity to model simplification. This may have been due to its more complex surface geometry. The maximum acceptable error was chosen to match the minimum detectable displacement of 0.05mm for this DF system [3]. DISCUSSION AND CONCLUSIONS The findings of this investigation identified differences in the error of cartilage surface proximities under loading due to the use of different mesh types and simplifications. The smoothing technique used by Amira did not consistently converge to a surface and the variable triangle size in Basic Simplification affected the computation of proximity, resulting in unpredictable error spikes in cartilage surface proximity calculations. The results suggest that surface modeling parameters are surface geometry specific. The limiting case of the medial tibial plateau showed the optimal simplification was 0.594mm triangle mesh side length (40% of the original faces). These results inform ongoing work toward an in-vivo pre-radiographic diagnostic of PTOA

DYNAMIC VALIDATION OF TIBIOFEMORAL KINEMATICS MEASURED USING A DUAL FLUOROSCOPY SYSTEM: A MARKER-BASED APPROACH

INTRODUCTION Knee joint cartilage degeneration in post-traumatic osteoarthritis is initiated at the point of injury and progresses through abnormal movement mechanics [1]. Anterior cruciate ligament rupture influences the development and progression of osteoarthritis [1], however the specific in vivo effects of abnormal bone and joint kinematics and kinetics on human cartilage health remain largely unknown.  Quantifying in vivo knee kinematics with submillimeter accuracies may elucidate injurious movement alterations.  Dual Fluoroscopy (DF) allows for accurate, high-speed, and non-invasive skeletal kinematics assessment, but requires validation.  The aim of this project was to quantify the in vitro accuracy and precision of a high-speed dual fluoroscopy system for measuring 6 degree of freedom (DOF) knee kinematics obtained from a marker-less 2D-3D registration approach as compared to the gold standard marker-based method. For this preliminary work, we hypothesized that the precision of inter-bead 3D Euclidean distance measurement is less than or equal to 0.10 mm [2]. METHODS Upon approval by the local ethics committee, one female cadaveric human leg was obtained through the local body donation program. Four 3mm metal beads were surgically implanted in the distal femur and proximal tibia.  Thereafter, the limb was scanned using computed tomography (CT). Following imaging, the soft tissues of the proximal shaft of the femur were dissected to expose the bone and the femoral head was removed. The proximal shaft of the femur was then fixed in a custom-made metal cylinder using fixation screws and potted using polymethyl methacrylate (PMMA). The free end of the metal cylinder was in turn fixed to an articulated 6 DOF tripod mount (Manfrotto, Italy).  In the DF laboratory the limb was suspended in the DF field of view using a custom steel frame. A rope pulley system, fixed around the ankle joint, was used to manipulate the limb. DF images were acquired at 60 Hz during manipulation of the limb into knee flexion. All images were distortion corrected and calibrated using established procedures. Marker-based tracking was conducted on 75 DF frames using in-house software to determine the 2D coordinates of the bead centroids in each image pair.  Subsequently, a modified direct linear transform was applied to obtain the 3D bead centroid coordinates. Matlab (MathWorks, v2014b, USA) code was written in order to determine the Euclidean distance between beads. RESULTSTable 1: The mean distance between beads in the femur and tibia ± SD (mm) calculated over 75 DF frames.  Right: Camera 1 DF image demonstrating the numbering of beads.DISCUSSION AND CONCLUSIONS The data indicated inter-bead distance variabilities consistent with previously observed system errors (for static imaging), when investigating a moving limb (Table 1). The observed variations could be due to multiple contributors. A lack of bead sphericity and bead deformation, as a result of surgical bead implantation, may have caused erroneous bead centroid estimates. Further, DF image distortions may have persisted even after distortion correction, contributing to observed error. Future steps include improved image calibration using a sophisticated bundle adjustment algorithm to further reduce system errors [3]

Concurrent validity and reliability of a semi-automated approach to measuring the magnetic resonance imaging morphology of the knee joint in active youth

Post-traumatic knee osteoarthritis is attributed to alterations in joint morphology, alignment, and biomechanics triggered by injury. While magnetic resonance (MR) imaging-based measures of joint morphology and alignment are relevant to understanding osteoarthritis risk, time consuming manual data extraction and measurement limit the number of outcomes that can be considered and deter widespread use. This paper describes the development and evaluation of a semi-automated software for measuring tibiofemoral and patellofemoral joint architecture using MR images from youth with and without a previous sport-related knee injury. After prompting users to identify and select key anatomical landmarks, the software can calculate 37 (14 tibiofemoral, 23 patellofemoral) relevant geometric features (morphology and alignment) based on established methods. To assess validity and reliability, 11 common geometric features were calculated from the knee MR images (proton density and proton density fat saturation sequences; 1.5 T) of 76 individuals with a 3-10-year history of youth sport-related knee injury and 76 uninjured controls. Spearman's or Pearson's correlation coefficients (95% CI) and Bland-Altman plots were used to assess the concurrent validity of the semi-automated software (novice rater) versus expert manual measurements, while intra-class correlation coefficients (ICC 2,1; 95%CI), standard error of measurement (95%CI), 95% minimal detectable change, and Bland-Altman plots were used to assess the inter-rater reliability of the semi-automated software (novice vs resident radiologist rater). Correlation coefficients ranged between 0.89 (0.84, 0.92; Lateral Trochlear Inclination) and 0.97 (0.96, 0.98; Patellar Tilt Angle). ICC estimates ranged between 0.79 (0.63, 0.88; Lateral Patellar Tilt Angle) and 0.98 (0.95, 0.99; Bisect Offset). Bland-Altman plots did not reveal systematic bias. These measurement properties estimates are equal, if not better than previously reported methods suggesting that this novel semi-automated software is an accurate, reliable, and efficient alternative method for measuring large numbers of geometric features of the tibiofemoral and patellofemoral joints from MR studies. </p

Patellofemoral joint geometry and osteoarthritis features 3–10 years after knee injury compared with uninjured knees

In this cross-sectional study, we compared patellofemoral geometry in individuals with a youth-sport-related intra-articular knee injury to uninjured individuals, and the association between patellofemoral geometry and magnetic resonance imaging (MRI)-defined osteoarthritis (OA) features. In the Youth Prevention of Early OA (PrE-OA) cohort, we assessed 10 patellofemoral geometry measures in individuals 3–10 years following injury compared with uninjured individuals of similar age, sex, and sport, using mixed effects linear regression. We also dichotomized geometry to identify extreme (&gt;1.96 standard deviations) features and assessed likelihood of having extreme values using Poisson regression. Finally, we evaluated the associations between patellofemoral geometry with MRI-defined OA features using restricted cubic spline regression. Mean patellofemoral geometry did not differ substantially between groups. However, compared with uninjured individuals, injured individuals were more likely to have extremely large sulcus angle (prevalence ratio [PR] 3.9 [95% confidence interval, CI: 2.3, 6.6]), and shallow lateral trochlear inclination (PR 4.3 (1.1, 17.9)) and trochlear depth (PR 5.3 (1.6, 17.4)). In both groups, high bisect offset (PR 1.7 [1.3, 2.1]) and sulcus angle (PR 4.0 [2.3, 7.0]) were associated with cartilage lesion, and most geometry measures were associated with at least one structural feature, especially cartilage lesions and osteophytes. We observed no interaction between geometry and injury. Certain patellofemoral geometry features are correlated with higher prevalence of structural lesions compared with injury alone, 3–10 years following knee injury. Hypotheses generated in this study, once further evaluated, could contribute to identifying higher-risk individuals who may benefit from targeted treatment aimed at preventing posttraumatic OA.</p

Establishing outcome measures in early knee osteoarthritis

The classification and monitoring of individuals with early knee osteoarthritis (OA) are important considerations for the design and evaluation of therapeutic interventions and require the identification of appropriate outcome measures. Potential outcome domains to assess for early OA include patient-reported outcomes (such as pain, function and quality of life), features of clinical examination (such as joint line tenderness and crepitus), objective measures of physical function, levels of physical activity, features of imaging modalities (such as of magnetic resonance imaging) and biochemical markers in body fluid. Patient characteristics such as adiposity and biomechanics of the knee could also have relevance to the assessment of early OA. Importantly, research is needed to enable the selection of outcome measures that are feasible, reliable and validated in individuals at risk of knee OA or with early knee OA. In this Perspectives article, potential outcome measures for early symptomatic knee OA are discussed, including those measures that could be of use in clinical practice and/or the research setting