Pradeeba Sridar1,2, Ashnil Kumar1,3, Ann Quinton2,4,5, Narelle June Kennedy2, Ralph Nanan2,5, Jinman Kim1,2,5. 1. 1School of Computer ScienceThe University of SydneySydneyNSW2006Australia. 2. 3Sydney Medical School NepeanThe University of SydneySydneyNSW2006Australia. 3. 2School of Biomedical EngineeringThe University of SydneySydneyNSW2006Australia. 4. 4School of Health, Medical and Applied SciencesCentral Queensland UniversitySydneyNSW2000Australia. 5. 5Charles Perkins CentreThe University of SydneySydneyNSW2006Australia.
Abstract
OBJECTIVE: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. METHOD: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B) -mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. RESULTS: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. CONCLUSION: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.
OBJECTIVE: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. METHOD: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B) -mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. RESULTS: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. CONCLUSION: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.
Authors: Nima Tajbakhsh; Jae Y Shin; Suryakanth R Gurudu; R Todd Hurst; Christopher B Kendall; Michael B Gotway Journal: IEEE Trans Med Imaging Date: 2016-03-07 Impact factor: 10.048
Authors: Meredith Schulson; Anthony Liu; Tracey Björkman; Ann Quinton; Kristy P Mann; Ron Benzie; Michael Peek; Ralph Nanan Journal: Front Surg Date: 2014-07-21