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Torres HR, Morais P, Oliveira B, Birdir C, Rüdiger M, Fonseca JC, Vilaça JL. A review of image processing methods for fetal head and brain analysis in ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106629. [PMID: 35065326 DOI: 10.1016/j.cmpb.2022.106629] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/20/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. METHODS In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. RESULTS For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. CONCLUSIONS A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.
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Affiliation(s)
- Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal.
| | - Pedro Morais
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Cahit Birdir
- Department of Gynecology and Obstetrics, University Hospital Carl Gustav Carus, TU Dresden, Germany; Saxony Center for Feto-Neonatal Health, TU Dresden, Germany
| | - Mario Rüdiger
- Department for Neonatology and Pediatric Intensive Care, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Jaime C Fonseca
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
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Drukker L, Sharma H, Droste R, Alsharid M, Chatelain P, Noble JA, Papageorghiou AT. Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video. Sci Rep 2021; 11:14109. [PMID: 34238950 PMCID: PMC8266837 DOI: 10.1038/s41598-021-92829-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 06/09/2021] [Indexed: 12/28/2022] Open
Abstract
Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer's eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.
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Affiliation(s)
- Lior Drukker
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Harshita Sharma
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Richard Droste
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Mohammad Alsharid
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Pierre Chatelain
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Aris T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
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Song C, Gao T, Wang H, Sudirman S, Zhang W, Zhu H. The Classification and Segmentation of Fetal Anatomies Ultrasound Image: A Survey. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Ultrasound imaging processing technology has been used in obstetric observation of the fetus and diagnosis of fetal diseases for more than half a century. It contains certain advantages and unique challenges which has been developed rapidly. From the perspective of ultrasound image analysis, at the very beginning, it is essential to determine fetal survival, gestational age and so on. Currently, the fetal anatomies ultrasound image analysis approaches have been studies and it has become an indispensable diagnostic tool for diagnosing fetal abnormalities, in order to gain more insight into the ongoing development of the fetus. Presently, it is the time to review previous approaches systematically in this field and to predict the directions of the future. Thus, this article reviews state-of-art approaches with the basic ideas, theories, pros and cons of ultrasound image technique for whole fetus with other anatomies. First of all, it summarizes the current pending problems and introduces the popular image processing methods, such as classification, segmentation etc. After that, the advantages and disadvantages in existing approaches as well as new research ideas are briefly discussed. Finally, the challenges and future trend are discussed.
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Affiliation(s)
- Chunlin Song
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
| | - Tao Gao
- Obstetrics and Gynecology, Wuxi People’s Hospital, Wuxi, Jiangsu, 214023, China
| | - Hong Wang
- BOE Technology Group Co. Ltd., Beijing, 100176, China
| | - Sud Sudirman
- Department of Computer Science, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Wei Zhang
- BOE Technology Group Co. Ltd., Beijing, 100176, China
| | - Haogang Zhu
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
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Skelton E, Matthew J, Li Y, Khanal B, Cerrolaza Martinez JJ, Toussaint N, Gupta C, Knight C, Kainz B, Hajnal JV, Rutherford M. Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison. Radiography (Lond) 2020; 27:519-526. [PMID: 33272825 PMCID: PMC8052189 DOI: 10.1016/j.radi.2020.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 11/20/2022]
Abstract
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.
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Affiliation(s)
- E Skelton
- Perinatal Imaging Department, King's College London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
| | - J Matthew
- Perinatal Imaging Department, King's College London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Y Li
- Department of Computing, Imperial College London, UK
| | - B Khanal
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | | | - N Toussaint
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - C Gupta
- Perinatal Imaging Department, King's College London, UK
| | - C Knight
- Perinatal Imaging Department, King's College London, UK; Guy's & St Thomas' NHS Foundation Trust, UK
| | - B Kainz
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Computing, Imperial College London, UK
| | - J V Hajnal
- Perinatal Imaging Department, King's College London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - M Rutherford
- Perinatal Imaging Department, King's College London, UK; Guy's & St Thomas' NHS Foundation Trust, UK
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Lin Z, Li S, Ni D, Liao Y, Wen H, Du J, Chen S, Wang T, Lei B. Multi-task learning for quality assessment of fetal head ultrasound images. Med Image Anal 2019; 58:101548. [PMID: 31525671 DOI: 10.1016/j.media.2019.101548] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 07/15/2019] [Accepted: 08/23/2019] [Indexed: 11/26/2022]
Abstract
It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subjective and depends on the clinical experience of sonographers. In order to deal with this challenge, we propose a new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment. MF R-CNN can identify the critical anatomical structure of the fetal head and analyze whether the magnification of the ultrasound image is appropriate, and then performs quality assessment of ultrasound images based on clinical protocols. Specifically, the first five convolution blocks of the MF R-CNN learn the features shared within the input data, which can be associated with the detection and classification tasks, and then extend to the task-specific output streams. In training, in order to speed up the different convergence of different tasks, we devise a section train method based on transfer learning. In addition, our proposed method also uses prior clinical and statistical knowledge to reduce the false detection rate. By identifying the key anatomical structure and magnification of the ultrasound image, we score the ultrasonic plane of fetal head to judge whether it is a standard image or not. Experimental results on our own-collected dataset show that our method can accurately make a quality assessment of an ultrasound plane within half a second. Our method achieves promising performance compared with state-of-the-art methods, which can improve the examination effectiveness and alleviate the measurement error caused by improper ultrasound scanning.
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Affiliation(s)
- Zehui Lin
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Shengli Li
- Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University, 3012 Fuqiang Rd, Shenzhen, 518060, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Yimei Liao
- Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University, 3012 Fuqiang Rd, Shenzhen, 518060, China
| | - Huaxuan Wen
- Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University, 3012 Fuqiang Rd, Shenzhen, 518060, China
| | - Jie Du
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Siping Chen
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.
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van den Heuvel TLA, Petros H, Santini S, de Korte CL, van Ginneken B. Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:773-785. [PMID: 30573305 DOI: 10.1016/j.ultrasmedbio.2018.09.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/05/2018] [Accepted: 09/14/2018] [Indexed: 06/09/2023]
Abstract
Ultrasound imaging remains out of reach for most pregnant women in developing countries because it requires a trained sonographer to acquire and interpret the images. We address this problem by presenting a system that can automatically estimate the fetal head circumference (HC) from data obtained with use of the obstetric sweep protocol (OSP). The OSP consists of multiple pre-defined sweeps with the ultrasound transducer over the abdomen of the pregnant woman. The OSP can be taught within a day to any health care worker without prior knowledge of ultrasound. An experienced sonographer acquired both the standard plane-to obtain the reference HC-and the OSP from 183 pregnant women in St. Luke's Hospital, Wolisso, Ethiopia. The OSP data, which will most likely not contain the standard plane, was used to automatically estimate HC using two fully convolutional neural networks. First, a VGG-Net-inspired network was trained to automatically detect the frames that contained the fetal head. Second, a U-net-inspired network was trained to automatically measure the HC for all frames in which the first network detected a fetal head. The HC was estimated from these frame measurements, and the curve of Hadlock was used to determine gestational age (GA). The results indicated that most automatically estimated GAs fell within the P2.5-P97.5 interval of the Hadlock curve compared with the GAs obtained from the reference HC, so it is possible to automatically estimate GA from OSP data. Our method therefore has potential application for providing maternal care in resource-constrained countries.
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Affiliation(s)
- Thomas L A van den Heuvel
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Medical Ultrasound Imaging Center, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Hezkiel Petros
- St. Luke's Catholic Hospital and College of Nursing and Midwifery, Wolisso, Ethiopia
| | - Stefano Santini
- St. Luke's Catholic Hospital and College of Nursing and Midwifery, Wolisso, Ethiopia
| | - Chris L de Korte
- St. Luke's Catholic Hospital and College of Nursing and Midwifery, Wolisso, Ethiopia; Physics of Fluids Group, MIRA, University of Twente, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer MEVIS, Bremen, Germany
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7
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van den Heuvel TLA, de Bruijn D, de Korte CL, van Ginneken B. Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One 2018; 13:e0200412. [PMID: 30138319 PMCID: PMC6107118 DOI: 10.1371/journal.pone.0200412] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 06/26/2018] [Indexed: 11/19/2022] Open
Abstract
In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, -0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.
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Affiliation(s)
- Thomas L. A. van den Heuvel
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Medical Ultrasound Imaging Center, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dagmar de Bruijn
- Department of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Chris L. de Korte
- Medical Ultrasound Imaging Center, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Fraunhofer MEVIS, Bremen, Germany
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8
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van den Heuvel TLA, de Bruijn D, de Korte CL, Ginneken BV. Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One 2018. [PMID: 30138319 DOI: 10.5281/zenodo.1327317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, -0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.
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Affiliation(s)
- Thomas L A van den Heuvel
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Medical Ultrasound Imaging Center, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dagmar de Bruijn
- Department of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Chris L de Korte
- Medical Ultrasound Imaging Center, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Fraunhofer MEVIS, Bremen, Germany
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Yaqub M, Kelly B, Papageorghiou AT, Noble JA. A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints. ULTRASOUND IN MEDICINE & BIOLOGY 2017; 43:2925-2933. [PMID: 28958729 DOI: 10.1016/j.ultrasmedbio.2017.07.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 06/19/2017] [Accepted: 07/17/2017] [Indexed: 06/07/2023]
Abstract
During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment.
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Affiliation(s)
- Mohammad Yaqub
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Brenda Kelly
- Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, UK
| | - Aris T Papageorghiou
- Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
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Koning IV, Roelants JA, Groenenberg IAL, Vermeulen MJ, Willemsen SP, Reiss IKM, Govaert PP, Steegers-Theunissen RPM, Dudink J. New Ultrasound Measurements to Bridge the Gap between Prenatal and Neonatal Brain Growth Assessment. AJNR Am J Neuroradiol 2017; 38:1807-1813. [PMID: 28663264 DOI: 10.3174/ajnr.a5278] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 04/24/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Most ultrasound markers for monitoring brain growth can only be used in either the prenatal or the postnatal period. We investigated whether corpus callosum length and corpus callosum-fastigium length could be used as markers for both prenatal and postnatal brain growth. MATERIALS AND METHODS A 3D ultrasound study embedded in the prospective Rotterdam Periconception Cohort was performed at 22, 26 and 32 weeks' gestational age in fetuses with fetal growth restriction, congenital heart defects, and controls. Postnatally, cranial ultrasound was performed at 42 weeks' postmenstrual age. First, reliability was evaluated. Second, associations between prenatal and postnatal corpus callosum and corpus callosum-fastigium length were investigated. Third, we created reference curves and compared corpus callosum and corpus callosum-fastigium length growth trajectories of controls with growth trajectories of fetuses with fetal growth retardation and congenital heart defects. RESULTS We included 199 fetuses; 22 with fetal growth retardation, 20 with congenital heart defects, and 157 controls. Reliability of both measurements was excellent (intraclass correlation coefficient ≥ 0.97). Corpus callosum growth trajectories were significantly decreased in fetuses with fetal growth restriction and congenital heart defects (β = -2.295; 95% CI, -3.320-1.270; P < .01; β = -1.267; 95% CI, -0.972-0.562; P < .01, respectively) compared with growth trajectories of controls. Corpus callosum-fastigium growth trajectories were decreased in fetuses with fetal growth restriction (β = -1.295; 95% CI, -2.595-0.003; P = .05). CONCLUSIONS Corpus callosum and corpus callosum-fastigium length may serve as reliable markers for monitoring brain growth from the prenatal into the postnatal period. The clinical applicability of these markers was established by the significantly different corpus callosum and corpus callosum-fastigium growth trajectories in fetuses at risk for abnormal brain growth compared with those of controls.
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Affiliation(s)
- I V Koning
- From the Department of Obstetrics and Gynecology (I.V.K., J.A.R., I.A.L.G., S.P.W., R.P.M.S.-T.), Division of Obstetrics and Prenatal Medicine.,Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - J A Roelants
- From the Department of Obstetrics and Gynecology (I.V.K., J.A.R., I.A.L.G., S.P.W., R.P.M.S.-T.), Division of Obstetrics and Prenatal Medicine.,Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - I A L Groenenberg
- From the Department of Obstetrics and Gynecology (I.V.K., J.A.R., I.A.L.G., S.P.W., R.P.M.S.-T.), Division of Obstetrics and Prenatal Medicine
| | - M J Vermeulen
- Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - S P Willemsen
- From the Department of Obstetrics and Gynecology (I.V.K., J.A.R., I.A.L.G., S.P.W., R.P.M.S.-T.), Division of Obstetrics and Prenatal Medicine.,Department of Biostatistics (S.P.W.), Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - I K M Reiss
- Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - P P Govaert
- Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands.,Department of Neonatology (P.P.G.), ZNA Koningin Paola Ziekenhuis, Antwerp, Belgium
| | - R P M Steegers-Theunissen
- From the Department of Obstetrics and Gynecology (I.V.K., J.A.R., I.A.L.G., S.P.W., R.P.M.S.-T.), Division of Obstetrics and Prenatal Medicine.,Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - J Dudink
- Department of Pediatrics (I.V.K., J.A.R., M.J.V., I.K.M.R., P.P.G., R.P.M.S.-T., J.D.), Division of Neonatology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands .,Department of Neonatology (J.D.), Wilhelmina's Children's Hospital, University Medical Center Utrecht, Utrecht, the Netherlands
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11
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Roelants JA, Koning IV, Raets MMA, Willemsen SP, Lequin MH, Steegers-Theunissen RPM, Reiss IKM, Vermeulen MJ, Govaert P, Dudink J. A New Ultrasound Marker for Bedside Monitoring of Preterm Brain Growth. AJNR Am J Neuroradiol 2016; 37:1516-22. [PMID: 26988817 DOI: 10.3174/ajnr.a4731] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 01/05/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Preterm neonates are at risk for neurodevelopmental impairment, but reliable, bedside-available markers to monitor preterm brain growth during hospital stay are still lacking. The aim of this study was to assess the feasibility of corpus callosum-fastigium length as a new cranial sonography marker for monitoring of preterm brain growth. MATERIALS AND METHODS In this longitudinal prospective cohort study, cranial ultrasound was planned on the day of birth, days 1, 2, 3, and 7 of life; and then weekly until discharge in preterm infants born before 29 weeks of gestational age. Reproducibility and associations between clinical variables and corpus callosum-fastigium growth trajectories were studied. RESULTS A series of 1-8 cranial ultrasounds was performed in 140 infants (median gestational age at birth, 27(+2) weeks (interquartile range, 26(+1) to 28(+1); 57.9% male infants). Corpus callosum-fastigium measurements showed good-to-excellent agreement for inter- and intraobserver reproducibility (intraclass correlation coefficient >0.89). Growth charts for preterm infants between 24 and 32 weeks of gestation were developed. Male sex and birth weight SD score were positively associated with corpus callosum-fastigium growth rate. CONCLUSIONS Corpus callosum-fastigium length measurement is a new reproducible marker applicable for bedside monitoring of preterm brain growth during neonatal intensive care stay.
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Affiliation(s)
- J A Roelants
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.) Departments of Obstetrics and Gynecology (J.A.R., I.V.K., S.P.W., R.P.M.S.-T.)
| | - I V Koning
- Departments of Obstetrics and Gynecology (J.A.R., I.V.K., S.P.W., R.P.M.S.-T.)
| | - M M A Raets
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.)
| | - S P Willemsen
- Departments of Obstetrics and Gynecology (J.A.R., I.V.K., S.P.W., R.P.M.S.-T.) Biostatistics (S.P.W.), Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - M H Lequin
- Department of Radiology (M.H.L.), University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - I K M Reiss
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.)
| | - M J Vermeulen
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.)
| | - P Govaert
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.)
| | - J Dudink
- From the Division of Neonatology (J.A.R., M.M.A.R., I.K.M.R., M.J.V., P.G., J.D.) Department of Radiology (J.D.) Intensive Care Unit (J.D.), Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
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12
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Liu Y, Dawant BM. Multi-modal Learning-based Pre-operative Targeting in Deep Brain Stimulation Procedures. ... IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS. IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS 2016; 2016:17-20. [PMID: 27754497 PMCID: PMC5042326 DOI: 10.1109/bhi.2016.7455824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation, as a primary surgical treatment for various neurological disorders, involves implanting electrodes to stimulate target nuclei within millimeter accuracy. Accurate pre-operative target selection is challenging due to the poor contrast in its surrounding region in MR images. In this paper, we present a learning-based method to automatically and rapidly localize the target using multi-modal images. A learning-based technique is applied first to spatially normalize the images in a common coordinate space. Given a point in this space, we extract a heterogeneous set of features that capture spatial and intensity contextual patterns at different scales in each image modality. Regression forests are used to learn a displacement vector of this point to the target. The target is predicted as a weighted aggregation of votes from various test samples, leading to a robust and accurate solution. We conduct five-fold cross validation using 100 subjects and compare our method to three indirect targeting methods, a state-of-the-art statistical atlas-based approach, and two variations of our method that use only a single modality image. With an overall error of 2.63±1.37mm, our method improves upon the single modality-based variations and statistically significantly outperforms the indirect targeting ones. Our technique matches state-of-the-art registration methods but operates on completely different principles. Both techniques can be used in tandem in processing pipelines operating on large databases or in the clinical flow for automated error detection.
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Affiliation(s)
- Yuan Liu
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Benoit M Dawant
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
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