Learning sonographic views using a multiple proposal approach
Keywords: Computer Vision
Machine Learning
Medical Image Analysis
Fetal ultrasound

Accurate fetal biometric measurement and diagnosis rely on the quality of acquisition of 2D ultrasound (US) diagnostic planes. Optimal imaging output mostly depends on the ability of the sonographer to identify and localise a set of key anatomical landmarks which normally exhibit certain geometrical relationship patterns. Localisation of structures in US can be challenging because of the variability of image appearance and pose with respect to the transducer. In this doctoral thesis, a multiple proposal approach is introduced to facilitate an automatic localisation of multiple anatomical structures. Inspired by the image acquisition process, the knowledge of the sonographer is modelled implicitly in a multiple-stage hypothesis learning framework based on a database of 2D fetal US images acquired from multiple arbitrary orientations. Information learned from each stage allows the model to incorporate geometric constraints in a structured manner which further helps to reduce the number of possible valid hypotheses due to the variety of viewpoints and articulations. In return, the multiple proposal framework ensures good recall and precise localisation of key anatomical structures on unconstrained images of arbitrary viewing angles.
There are a couple of key research contributions brought forward by this doctoral thesis. Firstly, a comprehensive dataset with annotated ground truth for respective anatomical structures in each image is explicitly described. This dataset includes images from multiple machines and sonographers’ expertise, thereby guaranteeing a collection of challenging images of various shapes, textures and layout configurations. An efficient coarse classifier based on integral channel features is then designed to provide automatic abdomen localisation. This first-stage proposal investigates a simple yet elegant implementation of a soft-cascade rejection threshold architecture with score recalibration and shows its impending practicality for US image object detection at potentially real-time speed. This improves object recall at a much-reduced time complexity.
Next, an automated segmentation-driven detection technique based on probabilistic superpixels-based Bag-of-Words (BOW) is presented. It is shown that this model, which is largely inspired by perceptual grouping of low level and mid-level image descriptors, is a good alternative to a conventional sliding-window mechanism in localising multiple key anatomical structures, such as the stomach and the umbilical vein (UV). This is achieved by reducing searching time and complexity compared to multi-scale sliding mechanism, and by jointly learning and modelling the spatial relationship of multi-class objects at training and testing time. Using this approach, the model transforms a pixel-wise probabilistic output into meaningful objects based on the knowledge of neighbouring superpixel regions, thus refining the location of these key structures. This method improves the average precision of the object detection (specifically for structures of deformable nature like the UV) by localising the exact object region as compared to the bounding boxes generated from the sliding-window approach.
Overall, this doctoral thesis has sought to investigate computer vision and machine learning techniques augmented by the knowledge of sonography for fetal US image analysis interpretation.