Dataset Information

Automated objective scoring of histologically apparent cartilage degeneration using a custom image analysis program.

ABSTRACT: Histologic assessment of cartilage degradation has traditionally involved semiquantitative techniques, the most commonly utilized being the Mankin scale. Such assessments depend on human observer subjectivity, and thus have drawn criticism on the basis of associated inter- and intraobserver variability. We report a newly developed computational image analysis procedure for fully automated and fully objective assessment of the Mankin scale. Image processing routines were developed in a widely used programming environment (Matlab) to analyze cartilage degradation. One hundred and twenty-five histology images incorporating a wide range of degradation features were analyzed by the algorithm and by seven observers experienced in cartilage histologic assessment. Based on random effects linear statistical models, the computer program performed well, showing a correlation of 0.88 between its Mankin scores and latent (average of human observers') image scores. Regarding the four subcomponents of the Mankin scale, computer program correlations with observer scores were best for surface defect and proteoglycan depletion, but less favorable for cellularity and tidemark invasion. While limitations exist with image processing techniques, the new algorithm provides an objective and automated method for analyzing cartilage histology sections, consistent with human observer grading.

SUBMITTER: Moussavi-Harami SF

PROVIDER: S-EPMC3682491 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Automated objective scoring of histologically apparent cartilage degeneration using a custom image analysis program.

Moussavi-Harami S Farshid SF Pedersen Douglas R DR Martin James A JA Hillis Stephen L SL Brown Thomas D TD

Journal of orthopaedic research : official publication of the Orthopaedic Research Society 20090401 4

Histologic assessment of cartilage degradation has traditionally involved semiquantitative techniques, the most commonly utilized being the Mankin scale. Such assessments depend on human observer subjectivity, and thus have drawn criticism on the basis of associated inter- and intraobserver variability. We report a newly developed computational image analysis procedure for fully automated and fully objective assessment of the Mankin scale. Image processing routines were developed in a widely use ...[more]

PMID: 18972361

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Project description:BackgroundThe Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.ObjectivesTo assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.MethodsImaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined image-based subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.ResultsIntraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).ConclusionsConvolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.

Project description:PurposeTo estimate airway tapering in control subjects and to assess the usability of tapering as a bronchiectasis biomarker in paediatric populations.MethodsAirway tapering values were semi-automatically quantified in 156 children with control CTs collected in the Normal Chest CT Study Group. Airway tapering as a biomarker for bronchiectasis was assessed on spirometer-guided inspiratory CTs from 12 patients with bronchiectasis and 12 age- and sex-matched controls. Semi-automatic image analysis software was used to quantify intra-branch tapering (reduction in airway diameter along the branch), inter-branch tapering (reduction in airway diameter before and after bifurcation) and airway-artery ratios on chest CTs. Biomarkers were further stratified in small, medium and large airways based on three equal groups of the accompanying vessel size.ResultsControl subjects showed intra-branch tapering of 1% and inter-branch tapering of 24-39%. Subjects with bronchiectasis showed significantly reduced intra-branch of 0.8% and inter-branch tapering of 19-32% and increased airway-artery ratios compared with controls (p < 0.01). Tapering measurements were significantly different between diseased and controls across all airway sizes. Difference in airway-artery ratio was only significant in small airways.ConclusionPaediatric normal values for airway tapering were established in control subjects. Tapering showed to be a promising biomarker for bronchiectasis as subjects with bronchiectasis show significantly less airway tapering across all airway sizes compared with controls. Detecting less tapering in larger airways could potentially lead to earlier diagnosis of bronchiectasis. Additionally, compared with the conventional airway-artery ratio, this novel biomarker has the advantage that it does not require pairing with pulmonary arteries.Key points• Tapering is a promising objective image biomarker for bronchiectasis that can be extracted semi-automatically and has good correlation with validated visual scoring methods. • Less airway tapering was observed in patients with bronchiectasis and can be observed sensitively throughout the bronchial tree, even in the more central airways. • Tapering values seemed to be less influenced by variety in scanning protocols and lung volume making it a more robust biomarker for bronchiectasis detection.

Dataset Information

Automated objective scoring of histologically apparent cartilage degeneration using a custom image analysis program.

Publications

Automated objective scoring of histologically apparent cartilage degeneration using a custom image analysis program.

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