Abstract:
Prostate delineation from MRI images is a prolonged challenging issue partially due to appearance variations across patients and disease progression. To address these cha...Show MoreMetadata
Abstract:
Prostate delineation from MRI images is a prolonged challenging issue partially due to appearance variations across patients and disease progression. To address these challenges, our proposed collaborative method takes into account the computed multiple label-relevance maps as multiple views for learning the optimal boundary delineation. In our method, we firstly extracted multiple label-relevance maps to represent the affinities between each unlabeled pixel to the pre-defined labels to avoid the selection of handcrafted features. Then these maps were incorporated in a collaborative clustering to learn the adaptive weights for an optimal segmentation which overcomes the seeds selection sensitivity problems. The segmentation results were evaluated over 22 prostate MRI patient studies with respect to dice similarity coefficient (DSC), absolute relative volume difference (ARVD) and average symmetric surface distance (ASSD) (mm). The results and t-Test demonstrated that the proposed method improved the segmentation accuracy and robustness and the improvement was statistically significant.
Published in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
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PubMed ID: 29060659