Random forest classification for hippocampal segmentation in 3D MR images

Rosalia Maglietta, Nicola Amoroso, Stefania Bruno, Andrea Chincarini, Giovanni Frisoni, Paolo Inglese, Sabina Tangaro, Andrea Tateo, Roberto Bellotti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Main goal of this paper is a detailed analysis of the performances of Random Forest algorithm in the field of automated hippocampalsegmentation using 3D MR Images. Fifty-six T1-weighted whole brain MR images were included in the study, together with the related manually segmented bilateral hippocampi (mask). Firstly, the relationship between manual and automated segmentations of hippocampus was explored using a number of standard metrics. For left (right) hemisphere the Dice's coefficient obtained by RF was 70.6% (68.4%). The structural complexity of 3D MR images is twofold. The amount of voxels per image is huge and the numbers of hippocampus and background voxels are strongly imbalanced. In order to overcome these two limitations, we propose two simple strategies: one consists of filtering the input data using the logical OR of the masks of training images, followed by the RF classification task, the other is constituted by learning the RF classifier plane by plane. Using both strategies, the segmentation performances of RF improve significantly and Dice's coefficients increases up to 79.1% (77.4%) for left (right) sides.

Original languageEnglish
Title of host publicationProceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
PublisherIEEE Computer Society
Pages264-267
Number of pages4
Volume1
DOIs
Publication statusPublished - 2013
Event2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States
Duration: Dec 4 2013Dec 7 2013

Other

Other2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
CountryUnited States
CityMiami, FL
Period12/4/1312/7/13

Keywords

  • hippocampal segmentation
  • MR Images
  • Random Forest

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Maglietta, R., Amoroso, N., Bruno, S., Chincarini, A., Frisoni, G., Inglese, P., Tangaro, S., Tateo, A., & Bellotti, R. (2013). Random forest classification for hippocampal segmentation in 3D MR images. In Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 (Vol. 1, pp. 264-267). [6784623] IEEE Computer Society. https://doi.org/10.1109/ICMLA.2013.53