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Endometrium assessment via thickness measurement is commonly performed in routine gynecological ultrasound examination for assessing the reproductive health of patients undergoing fertility related treatments and endometrium cancer screening in women with post-menopausal bleeding. This paper introduces a fully automated technique for endometrium thickness measurement from three-dimensional transvaginal ultrasound (TVUS) images. The algorithm combines the robustness of deep neural networks with the more interpretable level set method for segmentation. We propose a hybrid variational curve propagation model which embeds a deep-learned endometrium probability map in the segmentation energy functional. This solution provides approximately 30% performance improvement over a contemporary supervised learning method on a database of 59 TVUS images and the thickness measurement is found to be within ±2mm of the manual measurement in 87% of the cases.

We present a novel spatial planning system that automatically generates machining plans with stable fixtures to fabricate complex geometries. Given a cutting tool, the system initially finds all tool poses that do not cut into the desired part geometry, and constructs the maximal volume removable from a raw stock according to the tool's degrees of freedom. The planning space is then defined as the product space of all available tools and their associated maximal removal volumes. A sequence of machining operations to iteratively remove material from a raw stock is generated by searching the planning space and finding successive machining steps that minimize manufacturing time or cost. For each step, a vise or modular fixture is automatically assembled such that the part is stable and the fixture assembly does not interfere with tool motion. Process planning is an inverse problem and multiple feasible solutions exist to machine the part if it is deemed manufacturable by the system. The system generates and visualizes several qualitatively distinct fabrication plans for an engineer to choose from. The chosen plan is sent to a tool path planner to generate machining instructions. We will show several examples of rapid and automatic end-to-end machining process planning on complex geometries to demonstrate the scalability and practicality of our system.