Automatic segmentation of the intracranial volume in fetal MR images

N. Khalili, P. Moeskops, N.H.P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M.J.N.L. Benders, M.A. Viergever, J.P.W. Pluim, I. Išgum

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. This paper presents an automatic method for segmentation of the ICV in fetal MR images. The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. The method is developed and evaluated with 30 fetal T2-weighted MRI scans (average age 33.2\pm 1.2 weeks postmenstrual age). The set contains 10 scans acquired in axial, 10 in coronal and 10 in sagittal imaging planes. A reference standard was defined in all images by manual annotation of the intracranial volume in 10 equidistantly distributed slices. The automatic analysis was performed by training and testing the network using scans acquired in the representative imaging plane as well as combining the training data from all imaging planes. On average, the automatic method achieved Dice coefficients of 0.90 for the axial images, 0.90 for the coronal images and 0.92 for the sagittal images. Combining the training sets resulted in average Dice coefficients of 0.91 for the axial images, 0.95 for the coronal images, and 0.92 for the sagittal images. The results demonstrate that the evaluated method achieved good performance in extracting ICV in fetal MR scans regardless of the imaging plane.

Original languageEnglish
Title of host publicationFetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer
Pages42-51
Number of pages10
ISBN (Print)9783319675602
DOIs
Publication statusPublished - 2017
Event2017 International Workshop on Fetal and Infant Image Analysis, (FIFI 2017) - Quebec City, Canada
Duration: 14 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10554 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2017 International Workshop on Fetal and Infant Image Analysis, (FIFI 2017)
Abbreviated titleFIFI 2017
CountryCanada
CityQuebec City
Period14/09/1714/09/17

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