A Multi-Object Statistical Atlas Adaptive for Deformable Registration Errors in Anomalous Medical Image Segmentation

Author(s)

Martins, Samuel Botter ; Spina, Thiago Vallin ; Yasuda, Clarissa ; Falcao, Alexandre X. ; Styner, MA ; Angelini, ED

• Document type: Journal article
• Source: MEDICAL IMAGING 2017: IMAGE PROCESSING; v. 10133, p. 8-pg., 2017-01-01.

Abstract

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach. (AU)

• FAPESP's process: 15/09446-7 - Medical Image Segmentation: How to integrate object appearance/shape models and interactive correction with minimum user intervention?
• Grantee: Thiago Vallin Spina
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/11853-2 - SAMSAM: Segmentation for Analysis and Measurements in the Shoot Apical Meristem
• Grantee: Thiago Vallin Spina
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor