Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures
| Autor: | Gabriele Guidi, Elisa D'Angelo, N. Maffei, Bruno Meduri, Frank Lohr, G. Aluisio, Luca Fiorini, Patrizia Ferrazza, V. Vanoni |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Biophysics General Physics and Astronomy Breast Neoplasms Pattern Recognition Automated Imaging Three-Dimensional Similarity (network science) Atlas (anatomy) medicine Image Processing Computer-Assisted Cluster Analysis Humans Radiology Nuclear Medicine and imaging Segmentation Radiometry Lung Retrospective Studies Observer Variation Contouring Analysis of Variance business.industry Radiotherapy Planning Computer-Assisted Reproducibility of Results Pattern recognition Heart General Medicine Gold standard (test) Hierarchical clustering medicine.anatomical_structure Hausdorff distance Automatic segmentation Cardiac sub-structures Benchmark (computing) Female Artificial intelligence business Tomography X-Ray Computed |
| Zdroj: | Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB). 69 |
| ISSN: | 1724-191X |
| Popis: | Purpose Segmentation of cardiac sub-structures for dosimetric analyses is usually performed manually in time-consuming procedure. Automatic segmentation may facilitate large-scale retrospective analysis and adaptive radiotherapy. Various approaches, among them Hierarchical Clustering, were applied to improve performance of atlas-based segmentation (ABS). Methods Training dataset of ABS consisted of 36 manually contoured CT-scans. Twenty-five cardiac sub-structures were contoured as regions of interest (ROIs). Five auto-segmentation methods were compared: simultaneous automatic contouring of all 25 ROIs (Method-1); automatic contouring of all 25 ROIs using lungs as anatomical barriers (Method-2); automatic contouring of a single ROI for each contouring cycle (Method-3); hierarchical cluster-based automatic contouring (Method-4); simultaneous truth and performance level estimation (STAPLE). Results were evaluated on 10 patients. Dice similarity coefficient (DSC), average Hausdorff distance (AHD), volume comparison and physician score were used as validation metrics. Results Atlas performance improved increasing number of atlases. Among the five ABS methods, Hierarchical Clustering workflow showed a significant improvement maintaining a clinically acceptable time for contouring. Physician scoring was acceptable for 70% of the ROI automatically contoured. Inter-observer evaluation showed that contours obtained by Hierarchical Clustering method are statistically comparable with them obtained by a second, independent, expert contourer considering DSC. Considering AHD, distance from the gold standard is lower for ROIs segmented by ABS. Conclusions Hierarchical clustering resulted in best ABS results for the primarily investigated platforms and compared favorably to a second benchmark system. Auto-contouring of smaller structures, being in range of variation between manual contourers, may be ideal for large-scale retrospective dosimetric analysis. |
| Databáze: | OpenAIRE |
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