- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging.

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality.

Science for tomorrow's neurosurgery: insights on establishing a neurosurgery patient group focused on developing novel intra-operative imaging techniques.

Background: Incorporating patient and public involvement (PPI) in research is crucial for ensuring the relevance and success of studies, yet it remains significantly underutilised in surgical research.

Main Body: This commentary presents insights from our neurosurgical research team's experience with establishing and working with a PPI group called "Science for Tomorrow's Neurosurgery" on research regarding novel intra-operative optical imaging techniques. Through collaboration with patient-focused charities, we have successfully incorporated patient perspectives into our work at each stage of the research pipeline, whilst adhering to core PPI principles, such as reciprocal relationships, co-learning, partnerships, and transparency.

LoViT: Long Video Transformer for surgical phase recognition.

Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they train spatial feature extractors using frame-level supervision that could lead to incorrect predictions due to similar frames appearing at different phases, and poorly fuse local and global features due to computational constraints which can affect the analysis of long videos commonly encountered in surgical interventions. In this paper, we present a two-stage method, called Long Video Transformer (LoViT), emphasizing the development of a temporally-rich spatial feature extractor and a phase transition map.