Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison

TY - JOUR

T1 - Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions

T2 - A clinical evaluation and quality assessment comparison

AU - Skelton, E.

AU - Matthew, J.

AU - Li, Y.

AU - Khanal, B.

AU - Cerrolaza Martinez, J.j.

AU - Toussaint, N.

AU - Gupta, C.

AU - Knight, C.

AU - Kainz, B.

AU - Hajnal, J.V.

AU - Rutherford, M.

N1 - Funding Information: This work was supported by the Wellcome Trust Council IEH Award [102431] for the Intelligent Fetal Imaging and Diagnosis project (www.ifindproject.com) and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. The authors acknowledge support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The study has been granted NHS R&D and ethics approval, NRES ref no = 14/LO/1086 and 07/H0707/105. Funding Information: This work was supported by the Wellcome Trust Council IEH Award [ 102431 ] for the Intelligent Fetal Imaging and Diagnosis project ( www.ifindproject.com ) and the Wellcome/EPSRC Centre for Medical Engineering [ WT 203148/Z/16/Z ]. The authors acknowledge support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The study has been granted NHS R&D and ethics approval, NRES ref no = 14/LO/1086 and 07/H0707/105. Publisher Copyright: © 2020 The College of Radiographers Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - Introduction: Clinical evaluation of deep learning (DL) tools is essential to compliment technical accuracy metrics. This study assessed the image quality of standard fetal head planes automatically-extracted from three-dimensional (3D) ultrasound fetal head volumes using a customised DL-algorithm. Methods: Two observers retrospectively reviewed standard fetal head planes against pre-defined image quality criteria. Forty-eight images (29 transventricular, 19 transcerebellar) were selected from 91 transabdominal fetal scans (mean gestational age = 26 completed weeks, range = 20 +5–32 +3 weeks). Each had two-dimensional (2D) manually-acquired (2D-MA), 3D operator-selected (3D-OS) and 3D-DL automatically-acquired (3D-DL) images. The proportion of adequate images from each plane and modality, and the number of inadequate images per plane was compared for each method. Inter and intra-observer agreement of overall image quality was calculated. Results: Sixty-seven percent of 3D-OS and 3D-DL transventricular planes were adequate quality. Forty-five percent of 3D-OS and 55% of 3D-DL transcerebellar planes were adequate. Seventy-one percent of 3D-OS and 86% of 3D-DL transventricular planes failed with poor visualisation of intra-cranial structures. Eighty-six percent of 3D-OS and 80% of 3D-DL transcerebellar planes failed due to inadequate visualisation of cerebellar hemispheres. Image quality was significantly different between 2D and 3D, however, no significant difference between 3D-modalities was demonstrated (p < 0.005). Inter-observer agreement of transventricular plane adequacy was moderate for both 3D-modalities, and weak for transcerebellar planes. Conclusion: The 3D-DL algorithm can automatically extract standard fetal head planes from 3D-head volumes of comparable quality to operator-selected planes. Image quality in 3D is inferior to corresponding 2D planes, likely due to limitations with 3D-technology and acquisition technique. Implications for practice: Automated image extraction of standard planes from US-volumes could facilitate use of 3DUS in clinical practice, however image quality is dependent on the volume acquisition technique.

AB - Introduction: Clinical evaluation of deep learning (DL) tools is essential to compliment technical accuracy metrics. This study assessed the image quality of standard fetal head planes automatically-extracted from three-dimensional (3D) ultrasound fetal head volumes using a customised DL-algorithm. Methods: Two observers retrospectively reviewed standard fetal head planes against pre-defined image quality criteria. Forty-eight images (29 transventricular, 19 transcerebellar) were selected from 91 transabdominal fetal scans (mean gestational age = 26 completed weeks, range = 20 +5–32 +3 weeks). Each had two-dimensional (2D) manually-acquired (2D-MA), 3D operator-selected (3D-OS) and 3D-DL automatically-acquired (3D-DL) images. The proportion of adequate images from each plane and modality, and the number of inadequate images per plane was compared for each method. Inter and intra-observer agreement of overall image quality was calculated. Results: Sixty-seven percent of 3D-OS and 3D-DL transventricular planes were adequate quality. Forty-five percent of 3D-OS and 55% of 3D-DL transcerebellar planes were adequate. Seventy-one percent of 3D-OS and 86% of 3D-DL transventricular planes failed with poor visualisation of intra-cranial structures. Eighty-six percent of 3D-OS and 80% of 3D-DL transcerebellar planes failed due to inadequate visualisation of cerebellar hemispheres. Image quality was significantly different between 2D and 3D, however, no significant difference between 3D-modalities was demonstrated (p < 0.005). Inter-observer agreement of transventricular plane adequacy was moderate for both 3D-modalities, and weak for transcerebellar planes. Conclusion: The 3D-DL algorithm can automatically extract standard fetal head planes from 3D-head volumes of comparable quality to operator-selected planes. Image quality in 3D is inferior to corresponding 2D planes, likely due to limitations with 3D-technology and acquisition technique. Implications for practice: Automated image extraction of standard planes from US-volumes could facilitate use of 3DUS in clinical practice, however image quality is dependent on the volume acquisition technique.

UR - http://www.scopus.com/inward/record.url?scp=85097066075&partnerID=8YFLogxK

U2 - 10.1016/j.radi.2020.11.006

DO - 10.1016/j.radi.2020.11.006

M3 - Article

SN - 0033-8281

VL - 27

SP - 519

EP - 526

JO - Radiography

JF - Radiography

IS - 2

ER -