CMIC/WEISS Joint Seminar Series

Title: Multimodal Image Registration with Deep Neural Networks  

Abstract: Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. This talk presents deep-learning-based methods which address these aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration.  
Bio: Zac is a finishing-up PhD candidate at UCL within WEISS and CMIC. His thesis involves the development of interactive registration algorithms for limited or partial data scenarios, applied mainly to real-time interventional applications such as prostate biopsy.   

Link to the Moodle page here: https://moodle.ucl.ac.uk/course/view.php?id=19613   Please enrol with key: CMIC. 

Alternatively, you can use the zoom link below to go direct to the seminar: https://ucl.zoom.us/j/99464005163?pwd=ZFdURkJ4TjJIeGVhbXpTclhuNE9WUT09