Curved Planar Reformation (CPR) has proved to be a practical and widely used tool for the visualization of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for large, tubular, structures such as the trachea and the colon because abnormalities may be smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions and standard CPR for the surrounding tissue. This effectively combines gray scale contextual information with detailed color information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative to virtual endoscopy. Because the CPR and the volume rendering are tightly coupled, the projection method used has a significant effect on properties of the volume renderer, such as distortion and isometry. We describe and compare the different CPR projection methods and how they affect the volume rendering process. A version of the algorithm is also presented which makes use of importance driven techniques; this ensures the users attention is always focused on the area of interest and also improves the speed of the algorithm.

In this work, we propose a framework to collect a large-scale, diverse sign language dataset that can be used to train automatic sign language recognition models.The first contribution of this work is SDTRACK, a generic method for signer tracking and diarisation in the wild. Our second contribution is SEEHEAR, a dataset of 90 hours of British Sign Language (BSL) content featuring a wide range of signers, and including interviews, monologues and debates. Using SDTRACK, the SEEHEAR dataset is annotated with 35K active signing tracks, with corresponding signer identities and subtitles, and 40K automatically localised sign labels. As a third contribution, we provide benchmarks for signer diarisation and sign recognition on SEEHEAR.

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