City University of Hong Kong

Yan Xu

Walid Daoud

Jing Liao

Michael K H Leung

Hao Du

Qihua Dong

A weakly-supervised framework for segmenting a three-dimensional (3D) medical image incorporates information of geometric prior and contrastive similarity in a loss-based fashion for enhancing distinguishability of an organ in presence of complex organ shape and image artifacts (e.g., low-contrast tissues) in the image. Training images with bounding-box annotations are used to train the framework. The geometric prior is advantageously built on point-cloud representations, which are much more fine-grained and flexible than volume representations, thus serving as better supervision than an inherent property of the bounding-box annotations (i.e. height and width). Furthermore, using contrastive similarity encourages organ pixels to gather around in a contrastive embedding space. It helps more-effectively distinguish low-contrast tissues from the background than using the conventionally-used gray space.

Weakly-supervised 3D medical image segmentation using geometric prior and contrastive similarity

A method for making an aerogel includes the steps of a) soaking a wet gel having a liquid phase in at least one solvent to substantially replace the liquid phase in the wet gel with the solvent to form a soaked gel; b) freezing the soaked gel to form a frozen gel; and c) drying the frozen gel in a vacuum chamber to form an aerogel.

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Hong Kong, China

Yan Xu