Taxonomic Indexes for Automatic Prostate Segmentation on 3D MRI Scans Using Superpixels and Probabilistic Atlas
João Vitor Ferreira França, Giovanni L. F. da Silva, Pedro Thiago Cutrim dos Santos, Geraldo Bráz, Aristófanes Corrêa Silva, Elton Anderson Araujo de Cavalcanti · 2020
In prostate analysis clinical routine, manual and semi-manual techniques are used to interpret MRI scans, which in addition to being subjective, are time-consuming and operator-dependent. Prostate segmentation is necessary for the diagnosis of a possible tumor, biopsy, staging, monitoring and treatment. Thus, an automatic segmentation can be crucial for fast and reliable diagnosis, but is challenging to accomplish due to the varying sizes, shapes and also unclear prostate boundaries that are blended with surrounding tissues. Thereupon, this work proposes an automatic method for prostate segmentation on 3D MRI scans based on superpixels, taxonomic indexes, probabilistic atlas and the extreme gradient boosting algorithm (XGBoost). In addition, the proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a dice similarity coefficient of 84.03%, volumetric similarity of 95.42%, recall of 88.11%, specificity of 90.82%, and an accuracy of 90.06%. Experimental results demonstrate the high potential of the proposed method comparable to those previously published.