Automatic Segmentation of Posterior Fossa Structures in Pediatric Brain MRIs

Author(s)

Oliveira, Hugo ; Penteado, Larissa ; Maciel, Jose Luiz ; Ferraciolli, Suely Fazio ; Takahashi, Marcelo Straus ; Bloch, Isabelle ; Cesar Junior, Roberto ; IEEE Comp Soc

• Document type: Journal article
• Source: 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01.

Abstract

Pediatric brain MRI is a useful tool in assessing the healthy cerebral development of children. Since many pathologies may manifest in the brainstem and cerebellum, the objective of this study was to have an automated segmentation of pediatric posterior fossa structures. These pathologies include a myriad of etiologies from congenital malformations to tumors, which are very prevalent in this age group. We propose a pediatric brain MRI segmentation pipeline composed of preprocessing, semantic segmentation and post-processing steps. Segmentation modules are composed of two ensembles of networks: generalists and specialists. The generalist networks are responsible for locating and roughly segmenting the brain areas, yielding regions of interest for each target organ. Specialist networks can then improve the segmentation performance for underrepresented organs by learning only from the regions of interest from the generalist networks. At last, post-processing consists in merging the specialist and generalist networks predictions, and performing late fusion across the distinct architectures to generate a final prediction. We conduct a thorough ablation analysis on this pipeline and assess the superiority of the methodology in segmenting the brain stem, 4th ventricle and cerebellum. The proposed methodology achieved a macro-averaged Dice index of 0.855 with respect to manual segmentation, with only 32 labeled volumes used during training. Additionally, average distances between automatically and manually segmented surfaces remained around 1mm for the three structures, while volumetry results revealed high agreement between manually labeled and predicted regions. (AU)

• FAPESP's process: 19/16112-9 - Graph learning for MRI semantic segmentation
• Grantee: Larissa de Oliveira Penteado Dias
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/07386-5 - Segmentation of neonatal magnetic resonance imaging: a structural approach
• Grantee: Larissa de Oliveira Penteado Dias
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 20/06744-5 - Deep learning and intermediate representations for pediatric image analysis
• Grantee: Hugo Neves de Oliveira
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/50236-1 - Spatio temporal analysis of pediatric magnetic resonance images
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Regular Research Grants