Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks
File(s)
Accepted version
Author(s)
Sinclair, Matthew
Baumgartner, Christian F
Matthew, Jacqueline
Bai, Wenjia
Martinez, Juan Cerrolaza
Type
Conference Paper
Abstract
Measurement of head biometrics from fetal ultrasonography images is of key
importance in monitoring the healthy development of fetuses. However, the
accurate measurement of relevant anatomical structures is subject to large
inter-observer variability in the clinic. To address this issue, an automated
method utilizing Fully Convolutional Networks (FCN) is proposed to determine
measurements of fetal head circumference (HC) and biparietal diameter (BPD). An
FCN was trained on approximately 2000 2D ultrasound images of the head with
annotations provided by 45 different sonographers during routine screening
examinations to perform semantic segmentation of the head. An ellipse is fitted
to the resulting segmentation contours to mimic the annotation typically
produced by a sonographer. The model's performance was compared with
inter-observer variability, where two experts manually annotated 100 test
images. Mean absolute model-expert error was slightly better than
inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm
vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results
demonstrate that the model performs at a level similar to a human expert, and
learns to produce accurate predictions from a large dataset annotated by many
sonographers. Additionally, measurements are generated in near real-time at
15fps on a GPU, which could speed up clinical workflow for both skilled and
trainee sonographers.
importance in monitoring the healthy development of fetuses. However, the
accurate measurement of relevant anatomical structures is subject to large
inter-observer variability in the clinic. To address this issue, an automated
method utilizing Fully Convolutional Networks (FCN) is proposed to determine
measurements of fetal head circumference (HC) and biparietal diameter (BPD). An
FCN was trained on approximately 2000 2D ultrasound images of the head with
annotations provided by 45 different sonographers during routine screening
examinations to perform semantic segmentation of the head. An ellipse is fitted
to the resulting segmentation contours to mimic the annotation typically
produced by a sonographer. The model's performance was compared with
inter-observer variability, where two experts manually annotated 100 test
images. Mean absolute model-expert error was slightly better than
inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm
vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results
demonstrate that the model performs at a level similar to a human expert, and
learns to produce accurate predictions from a large dataset annotated by many
sonographers. Additionally, measurements are generated in near real-time at
15fps on a GPU, which could speed up clinical workflow for both skilled and
trainee sonographers.
Date Issued
2018-10-28
Date Acceptance
2018-04-24
Citation
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp.714-717
Start Page
714
End Page
717
Journal / Book Title
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Copyright Statement
Copyright © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://arxiv.org/abs/1804.09102v1
Source
International Engineering in Medicine and Biology Conference
Subjects
cs.CV
cs.CV
Notes
EMBC 2018
Publication Status
Published
Start Date
2018-07-18
Finish Date
2018-07-21
Coverage Spatial
Honolulu, HI, USA