An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

Charlens Álvarez; Fabio Martínez; Eduardo Romero

doi:10.1117/12.2073449

28 January 2015 An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

Charlens Álvarez, Fabio Martínez, Eduardo Romero

Author Affiliations +

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 92870D (2015) https://doi.org/10.1117/12.2073449
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

Citation Download Citation

Charlens Álvarez, Fabio Martínez, and Eduardo Romero "An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92870D (28 January 2015); https://doi.org/10.1117/12.2073449

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