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个人简介
I teach the Geometric Modelling course where we build interesting 3D models.
I work in the general area of 3D geometric modelling, with a particular focus on the analysis of images sourced from medical scanners, across multiple modalities.
My group's OxMedIS software for Medical Image Segmentation is capable of processing MRI, CT, microCT, ultrasound and other medical and biological data. We have developed algorithms to partition each 3D image into regions with similar properties. These images are then analysed further so that particular features (such as bones, organs or blood vessels) can be identified within them and segmented out. Once labelled, the features can be reconstructed as 3D shapes and displayed or 3D printed on our Makerbot 2X. We specialise in automating some of the steps of the segmentation, whilst providing the clinicians with a manual override on the final results. Some of our algorithms work inparallel and have been implemented on a GPU.
The segmentation methods have been validated against hand-drawn ground truth contours, using a suite of conventional evaluation metrics. In addition we have also designed our own evaluation metrics which we have proved to rank segmentation results effectively.
Our most recent experiments have successfully used machine learning tehniques (specifically reinforcement learning) in order to segment features out of a pre-partitioned image. We thus reduce the need for vast amounts oftime consuming hand-drawn training contours.
I work in the general area of 3D geometric modelling, with a particular focus on the analysis of images sourced from medical scanners, across multiple modalities.
My group's OxMedIS software for Medical Image Segmentation is capable of processing MRI, CT, microCT, ultrasound and other medical and biological data. We have developed algorithms to partition each 3D image into regions with similar properties. These images are then analysed further so that particular features (such as bones, organs or blood vessels) can be identified within them and segmented out. Once labelled, the features can be reconstructed as 3D shapes and displayed or 3D printed on our Makerbot 2X. We specialise in automating some of the steps of the segmentation, whilst providing the clinicians with a manual override on the final results. Some of our algorithms work inparallel and have been implemented on a GPU.
The segmentation methods have been validated against hand-drawn ground truth contours, using a suite of conventional evaluation metrics. In addition we have also designed our own evaluation metrics which we have proved to rank segmentation results effectively.
Our most recent experiments have successfully used machine learning tehniques (specifically reinforcement learning) in order to segment features out of a pre-partitioned image. We thus reduce the need for vast amounts oftime consuming hand-drawn training contours.
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APPLIED SCIENCES-BASELno. 13 (2023): 7966-7966
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DATA ENGINEERING IN MEDICAL IMAGING, DEMI 2023 (2023): 1-10
MILLanD@MICCAIpp.181-190, (2023)
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MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2022 (2022): 494-507
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