Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement
| Autor: | Hrishikesh Haldankar, Peter L. Choyke, Sergei V. Fotin, Naira Muradyan, Justin Kunz, Yin Yin, Senthil Periaswamy, François Cornud, Baris Turkbey |
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| Rok vydání: | 2012 |
| Předmět: |
medicine.diagnostic_test
Cross-correlation Computer science business.industry medicine.medical_treatment Initialization Magnetic resonance imaging Radiation therapy medicine.anatomical_structure Prostate Biopsy medicine Computer vision Segmentation Artificial intelligence business Classifier (UML) Image resolution Prostate segmentation |
| Zdroj: | Medical Imaging: Image Processing |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.911758 |
| Popis: | Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution. |
| Databáze: | OpenAIRE |
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