IEEE International Symposium Biomedical Imaging, ISBI 2015, New York
Shape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effec-tive use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process
accordingly. We evaluate our proposed approach on 2-photon mi-croscopy images containing dendritic spines and compare its perfor-mance quantitatively to an existing approach based on nonparamet-ric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation.