Brain CT Segmentor
Project Overview
Challenges
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University
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About
Brain CT Segmentor utilises AI tools to enhance detection and monitoring of neurological diseases from CT scans. Traditional radiology can over-estimate lesion volumes and faces challenges like lengthy image segmentation and report creation. This technology leverages modern computer vision models to streamline these processes. Not only can it validate manual analyses by radiologists, but it also swiftly and accurately measures tissue volumes. Besides identifying intracranial haemorrhage and segmenting brain tissue, it auto-generates radiology reports. Favouring CT over pricier MRI, it offers an economic advantage, particularly benefiting emerging markets.
Team
Chun Hung How
Nanyang Technological University
I am a senior data scientist in Singapore healthcare for several years. I have recently transitioned into a researcher role during my MEng working on medical imaging AI. Throughout my research, I had the opportunity to work with an amazing team in building an AI tool for brain lesion detection, eventually ventured to building a prototype for neuroradiologists. I love being engaged in the AI community and be a part of the tremendous transformation in healthcare now.
Yi Hao Chan
Nanyang Technological University
I am an AI researcher working on developing deep learning models for brain imaging analysis. During my PhD studies, I developed numerous techniques to extract disease biomarkers from structural and functional magnetic resonance images. My background is in Business and Computer Science, and I have worked in various roles in the healthcare industry. Currently, I am exploring how my research can be translated into tools that can be used clinically and hope to form startups from them to serve urgent clinical needs.
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