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Title: Delving deep into fetal neurosonography : an image analysis approach
Author: Huang, Ruobing
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Ultrasound screening has been used for decades as the main modality to examine fetal brain development and to diagnose possible anomalies. However, basic clinical ultrasound examination of the fetal head is limited to axial planes of the brain and linear measurements which may have restrained its potential and efficacy. The recent introduction of three-dimensional (3D) ultrasound provides the opportunity to navigate to different anatomical planes and to evaluate structures in 3D within the developing brain. Regardless of acquisition methods, interpreting 2D/3D ultrasound fetal brain images require considerable skill and time. In this thesis, a series of automatic image analysis algorithms are proposed that exploit the rich sonographic patterns captured by the scans and help to simplify clinical examination. The original contributions include: 1. An original skull detection method for 3D ultrasound images, which achieves mean accuracy of 2.2 ± 1.6 mm compared to the ground truth (GT). In addition, the algorithm is utilised for accurate automated measurement of essential biometry in standard examinations: biparietal diameter (mean accuracy: 2.1 ± 1.4 mm) and head circumference (mean accuracy: 4.5 ± 3.7 mm). 2. A plane detection algorithm. It automatically extracts mid-sagittal plane that provides visualization of midline structures, which are crucial to assess central nervous system malformations. The automated planes are in accordance with manual ones (within 3.0 ± 3.5°). 3. A general segmentation framework for delineating fetal brain structures in 2D images. The automatically generated predictions are found to be agreed with the manual delineations (mean dice-similarity coefficient: 0.79 ± 0.07). As a by-product, the algorithm generated automated biometry. The results might be further utilized for morphological evaluation in future research. 4. An efficient localization model that is able to pinpoint the 3D locations of five key brain structures that are examined in a routine clinical examination. The predictions correlate with the ground truth: the average centre deviation is 1.8 ± 1.4 mm, and the size difference between them is 1.9 ± 1.5 mm. The application of this model may greatly reduce the time required for routine examination in clinical practice. 5. A 3D affine registration pipeline. Leveraging the power of convolutional neural networks, the model takes raw 3D brain images as input and geometrically transforms fetal brains into a unified coordinate system (proposed as a Fetal Brain Talairach system). The integration of these algorithms into computer-assisted analysis tools may greatly reduce the time and effort to evaluate 3D fetal neurosonography for clinicians. Furthermore, they will assist understanding of fetal brain maturation by distilling 2D/3D information directly from the uterus.
Supervisor: Noble, J. Alison Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Image analysis ; Machine learning ; Fetal brain ; Ultrasound