Computerised image analysis of musculoskeletal diagnostics and surgical planning

Summary

This research opportunity covers the projects for Diagnostic Imaging Theme of the ARC Centre for Innovative BioEngineering,  a multidisciplinary collaboration among researchers at The University of Sydney, University of Technology Sydney, Swinburne University of Technology, and Beth Israel Deaconess Medical Centre, in collaboration with leading industry partners including Allegra Orthopaedics, Osseointegration International Pty Ltd, Peter Brehm GmbH, Ti2Medical, and Royal Prince Alfred Hospital. The research will involve working closely with academics and industry partners, gaining technical, translational, commercialisation, and entrepreneurial skills to overcome industry-focused challenges in biomedical engineering.

Supervisor(s)

Associate Professor Jinman Kim, Professor David Feng, Dr Ashnil Kumar

Research Location

Computer Science

Program Type

Masters/PHD

Synopsis

The research projects are aimed at developing computerised image processing technologies for use in a variety of musculoskeletal (bone and muscle) clinical applications, including identification and analysis of bone defects, computer-aided diagnostic and surgical planning tools, and design of implant structures. Students will research the extension of state-of-the-art computerised image analysis technologies (such as deep learning) to derive new solutions for these challenges.

Additional Information

There are three topics available as part of this research opportunity (listed below). Scholarships are available for all topics – see https://arctcibe.org/apply-for-scholarship/ . TOPIC 1: Deep neural networks for omni-modality musculoskeletal (MSK) image analysis Description: The discovery of biomarkers requires accurate delineation of bone and tissues surrounding a MSK defect, but this is difficult because different types of images (x-ray, PET, CT, MRI) depict different characteristics. We will derive a computerised image segmentation algorithm to automatically delineate bones and musculature surrounding the defect, using state-of-the-art convolutional neural networks (CNNs) – a data-driven approach to identify the quantifiable image characteristics that are most relevant for a particular task – in this case, segmentation. The key challenge will be to train the CNNs across all image types (both functional and anatomical) to identify the correlations between them so that bones and muscles can be optimally delineated regardless of the image type. The outcomes will be techniques to improve diagnostic processes by allowing automated localisation and biomarker analysis of the anatomical defect sites.
Project eligibility: Interest and experience in the area of image analysis algorithms. Familiarity with image processing and/or biology would be beneficial.TOPIC 2: Advanced 3D visualisation of musculoskeletal (MSK) imaging
Description: Clinicians and surgeons need to interpret imaging data for diagnosis and pre-surgical planning. However, viewing the images directly is not optimal because the defect has a non-trivial risk of being obscured by noise or being occluded by other structures. The project will involve research on 3D graphics optimisations to create an algorithm that exploits graphics processing hardware to enable 3D visualisation of the anatomical defect on a computer display. The outcome will be a new 3D visualisation algorithm that enables improved diagnosis and pre-surgical planning, by allowing clinicians to view the anatomical characteristics of the MSK defect without the noise and obstruction inherent in the medical images.
Project eligibility: Interest and experience is in the area of image visualisation algorithms. Familiarity with 3D graphics programming (CUDA) or image processing would be beneficial.TOPIC 3: Advanced segmentation of musculoskeletal (MSK) imaging and surgical planning
Description: With the advent of imaging and navigation technologies, pre-operative planning and subsequent execution of these plans has become a reality in knee replacement surgery.  However, current approach to semi-automated / manual segmentation of CT scans and manipulation to complete the surgical plan is a time consuming and inexact process. The project will involve research on the state-of-the-art deep learning and shape modelling based segmentation algorithms and their application CT scans of the knee. In particular, quantitative measurements from the tracking of changes in 3D models from preoperative to postoperative scans will be explored.  The student will be involved in the planning of knee replacement surgeries using a novel instrument platform and assess the surgical result by comparing the postoperative CT scan of the surgery to the plan.
Project eligibility: Interest and experience in the area of image analysis algorithms. Familiarity with image processing and/or biology would be beneficial.

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Keywords

computerised image processing, 3D image visualisation, medical image analysis, deep learning, Machine learning, graphics, musculoskeletal imaging, Image segmentation, computed tomography, computed aided diagnosis, surgical planning, surgical simulations

Opportunity ID

The opportunity ID for this research opportunity is: 2426

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