Deep recurrent-convolutional model for automated segmentation of craniomaxillofacial CT scans

F. Murabito*, S. Palazzo, F. Proietto Salanitri, F. Rundo, U. Bagci, D. Giordano, R. Leonardi, C. Spampinato

*Corresponding author for this work

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

Abstract

In this paper we define a deep learning architecture for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT scans that leverages the recent success of encoder-decoder models for semantic segmentation of natural images. In particular, we propose a fully convolutional deep network that combines the advantages of recent fully convolutional models, such as Tiramisu, with squeeze-and-excitation blocks for feature recalibration, integrated with convolutional LSTMs to model spatio-temporal correlations between consecutive slices. The proposed segmentation network shows superior performance and generalization capabilities (to different structures and imaging modalities) than state of the art methods on automated segmentation of CMF structures (e.g., mandibles and airways) in several standard benchmarks (e.g., MICCAI datasets) and on new datasets proposed herein, effectively facing shape variability.

Original languageEnglish (US)
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10644-10649
Number of pages6
ISBN (Electronic)9781728188089
DOIs
StatePublished - 2020
Externally publishedYes
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period1/10/211/15/21

Keywords

  • Fully convolutional neural networks
  • Mandibles
  • Pharyngeal airways
  • Squeeze-and-excitation residual layers

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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