Deep Learning Automatic Fetal Structures Segmentation in MRI Scans with Few Annotated Datasets

Gal Dudovitch, Daphna Link-Sourani, Liat Ben Sira, Elka Miller, Dafna Ben Bashat, Leo Joskowicz

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

Abstract

We present a new method for end-to-end automatic volumetric segmentation of fetal structures in MRI scans with deep learning networks trained with very few annotated scans. It consists of three main stages: 1) two-step automatic structure segmentation with custom 3D U-Nets; 2) segmentation error estimation, and; 3) segmentation error correction. The automatic structure segmentation stage first computes a region of interest (ROI) on a downscaled scan and then computes a final segmentation on the cropped ROI. The segmentation error estimation stage uses prediction-time augmentations of the input scan to compute multiple segmentations and estimate the segmentation uncertainty for individual slices and for the entire scan. The segmentation error correction stage then uses these estimations to locate the most error-prone slices and to correct the segmentations in those slices based on validated adjacent slices. Experimental results of our methods on fetal body (63 cases, 9 for training, 55 for testing) and fetal brain MRI scans (35 cases, 6 for training, 29 for testing) yield a mean Dice coefficient of 0.96 for both, and a mean Average Symmetric Surface Distance of 0.74 mm and 0.19 mm, respectively, below the observer delineation variability.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages365-374
Number of pages10
ISBN (Print)9783030597245
DOIs
StatePublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12266 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Deep learning
  • Fetal MRI
  • Segmentation
  • Uncertainty estimation

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