Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. Conclusions: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.
Objective To evaluate the utility of subchondral bone texture from a baseline x-ray image for predicting 3-year knee osteoarthritis (OA) progression. Methods A total of 138 participants in the Prediction of Osteoarthritis Progression (POP) study were evaluated at baseline and 3 years. Fixed-flexion knee radiographs of the 248 non-replaced knees underwent fractal analysis of the medial subchondral tibial plateau using a commercially available software tool. OA progression was defined as a 1-grade change in joint space narrowing (JSN) or osteophyte based on a standardized knee atlas. Statistical analysis of fractal signatures was performed using a new method based on modeling the overall shape of fractal dimension versus radius curves. Results Baseline fractal signature of the medial tibial plateau was predictive of medial knee JSN progression (area under the curve [AUC] of Receiver Operating Characteristic plot of 0.75), but not progression based on osteophyte or progression of the lateral compartment. The traditional covariates (age, gender, body mass index, knee pain), general bone mineral content, and baseline joint space width fared little better than random variables for predicting OA progression (AUC 0.52–0.58). The maximal predictive model combined baseline fractal signature, knee alignment, traditional covariates, and bone mineral content (AUC 0.79). Conclusions We identified a prognostic marker of OA that is readily extracted from a plain radiograph by fractal signature analysis. The global shape approach to analyzing these data is a potentially efficient means of identifying individuals at risk of knee OA progression that needs to be validated in a second cohort.
ObjectivesOsteoarthritis (OA) structural status is imperfectly classified using radiographic assessment. Statistical shape modelling (SSM), a form of machine-learning, provides precise quantification of a characteristic 3D OA bone shape. We aimed to determine the benefits of this novel measure of OA status for assessing risks of clinically important outcomes.MethodsThe study used 4796 individuals from the Osteoarthritis Initiative cohort. SSM-derived femur bone shape (B-score) was measured from all 9433 baseline knee MRIs. We examined the relationship between B-score, radiographic Kellgren-Lawrence grade (KLG) and current and future pain and function as well as total knee replacement (TKR) up to 8 years.ResultsB-score repeatability supported 40 discrete grades. KLG and B-score were both associated with risk of current and future pain, functional limitation and TKR; logistic regression curves were similar. However, each KLG included a wide range of B-scores. For example, for KLG3, risk of pain was 34.4 (95% CI 31.7 to 37.0)%, but B-scores within KLG3 knees ranged from 0 to 6; for B-score 0, risk was 17.0 (16.1 to 17.9)% while for B-score 6, it was 52.1 (48.8 to 55.4)%. For TKR, KLG3 risk was 15.3 (13.3 to 17.3)%; while B-score 0 had negligible risk, B-score 6 risk was 35.6 (31.8 to 39.6)%. Age, sex and body mass index had negligible effects on association between B-score and symptoms.ConclusionsB-score provides reader-independent quantification using a single time-point, providing unambiguous OA status with defined clinical risks across the whole range of disease including pre-radiographic OA. B-score heralds a step-change in OA stratification for interventions and improved personalised assessment, analogous to the T-score in osteoporosis.
Objective To evaluate subchondral bone trabecular integrity (BTI) from a radiograph as a predictor of knee osteoarthritis (OA) progression. Methods Longitudinal (baseline, 12- and 24-month) knee radiographs were available from 60 female subjects with knee OA. OA progression was defined by 12- and 24-month change in radiographic medial compartment minimal joint space width (JSW) and medial joint space area (JSA), and medial tibial and femoral cartilage volume from magnetic resonance imaging. Bone Trabecular Integrity (BTI) of the medial tibial plateau was analyzed by fractal signature analysis with a commercially available software. Receiver Operating Characteristic curves of BTI were used to predict 5% change in OA progression parameters. Results Individual terms (linear and quadratic) of baseline BTI of vertical trabeculae predicted knee OA progression based on 12- and 24-month change in JSA (p<0.01 for 24 months), 24-month change in tibial (p<0.05) but not femoral cartilage volume, and 24-month change in JSW (p=0.05). ROC utilizing both terms of baseline BTI predicted 5% change in the OA progression parameters over 24 months with high accuracy as reflected by the area under the curve (AUC) measures: JSW 81%, JSA 85%, tibial 75% and femoral 85% cartilage volume. Change in BTI was also significantly associated (p<0.05) with concurrent change in JSA over 12 and 24 months and change in tibial cartilage volume over 24 months. Conclusions BTI predicts structural OA progression as determined by radiographic and MRI outcomes. BTI may therefore be worthy of study as an outcome measure for OA studies and clinical trials.
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