- 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.

Generating multi-pathological and multi-modal images and labels for brain MRI.

The last few years have seen a boom in using generative models to augment real datasets, as synthetic data can effectively model real data distributions and provide privacy-preserving, shareable datasets that can be used to train deep learning models. However, most of these methods are 2D and provide synthetic datasets that come, at most, with categorical annotations. The generation of paired images and segmentation samples that can be used in downstream, supervised segmentation tasks remains fairly uncharted territory.

Patterns of Multidimensional Social Exclusion Among Older Home Care Services Recipients.

Objectives: The guiding principle of current aging policies has been to promote older adults to live in their private homes, but little attention has been paid to social exclusion of older adults receiving home-based care. The aim of this study is to increase understanding on different patterns of multidimensional social exclusion among older adults receiving formal home care services, and through this to shed light on the possible challenges of current aging-in-place policies.

Methods: The survey data were collected in 2022 among older adults aged 65 to 102 years receiving home care services in Finland and merged with administrative data (n = 733).

Exclusion of non-Involved uterus from the target volume (EXIT-trial): An individualized treatment for locally advanced cervical cancer using modern radiotherapy and imaging techniques followed by completion surgery.

Background And Purpose: Chemoradiotherapy followed by brachytherapy is the standard of care for locally advanced cervical cancer (LACC). In this study, we postulate that omitting an iconographical unaffected uterus (+12 mm distance from the tumour) from the treatment volume is safe and that no tumour will be found in the non-targeted uterus (NTU) leading to reduction of high-dose volumes of surrounding organs at risk (OARs).

Material And Methods: In this single-arm phase 2 study, two sets of target volumes were delineated: one standard-volume (whole uterus) and an EXIT-volume (exclusion of non-tumour-bearing parts of the uterus with a minimum 12 mm margin from the tumour).

MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images.

The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets. Through MONAI Label, researchers can develop AI annotation applications focusing on their domain of expertise.

Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter.

Purpose: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves competitive performance with training on limited datasets, while also enhancing model robustness in various surgical scenarios.

Methods: We propose a method that harnesses the strengths of pre-trained Vision Transformers (ViTs) and data efficiency of convolutional neural networks (CNNs).

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations.

We introduce MHVAE, a deep hierarchical variational autoencoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images.

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin-based phantoms and Monte Carlo experiments.

Information about tissue oxygen saturation (StO) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non-contact imaging. Three analytical optical reflectance models for homogeneous, semi-infinite, tissue have been proposed (Modified Beer-Lambert, Jacques 1999, Yudovsky 2009) but these have not been directly compared for tissue parameter extraction purposes. We compare these analytical models using Monte Carlo (MC) simulated diffuse reflectance spectra and controlled gelatin-based phantoms with measured diffuse reflectance spectra and known ground truth composition parameters.

Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset.

Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging.

Unpaired intra-operative OCT (iOCT) video super-resolution with contrastive learning.

Regenerative therapies show promise in reversing sight loss caused by degenerative eye diseases. Their precise subretinal delivery can be facilitated by robotic systems alongside with Intra-operative Optical Coherence Tomography (iOCT). However, iOCT's real-time retinal layer information is compromised by inferior image quality.

Disease Control and Late Toxicity in Adaptive Dose Painting by Numbers Versus Nonadaptive Radiation Therapy for Head and Neck Cancer: A Randomized Controlled Phase 2 Trial.

Purpose: Local recurrence remains the main cause of death in stage III-IV nonmetastatic head and neck cancer (HNC), with relapse-prone regions within high F-fluorodeoxyglucose positron emission tomography (F-FDG-PET)-signal gross tumor volume. We investigated if dose escalation within this subvolume combined with a 3-phase treatment adaptation could increase local (LC) and regional (RC) control at equal or minimized radiation-induced toxicity, by comparing adaptive F-FDG-PET voxel intensity-based dose painting by numbers (A-DPBN) with nonadaptive standard intensity modulated radiation therapy (S-IMRT).

Methods And Materials: This 2-center randomized controlled phase 2 trial assigned (1:1) patients to receive A-DPBN or S-IMRT (+/-chemotherapy).

High-fidelity, low-cost synthetic training model for fetoscopic spina bifida repair.

Background: Fetoscopic spina bifida repair is increasingly being practiced, but limited skill acquisition poses a barrier to widespread adoption. Extensive training in relevant models, including both ex vivo and in vivo models may help. To address this, a synthetic training model that is affordable, realistic, and that allows skill analysis would be useful.

Opto-Lipidomics of Tissues.

Lipid metabolism and signaling play pivotal functions in biology and disease development. Despite this, currently available optical techniques are limited in their ability to directly visualize the lipidome in tissues. In this study, opto-lipidomics, a new approach to optical molecular tissue imaging is introduced.

3D vs. 2D simulated fetoscopy for spina bifida repair: a quantitative motion analysis.

3D imaging technology is becoming more prominent every day. However, more validation is needed to understand the actual benefit of 3D versus conventional 2D vision. This work quantitatively investigates whether experts benefit from 3D vision during minimally invasive fetoscopic spina bifida (fSB) repair.

Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker.

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance.

Methods: First, long-term dependency is encoded by transformation positions within a frame sequence.

Erratum to "Optical Detection of Distal Lung Enzyme Activity in Human Inflammatory Lung Disease".

[This corrects the article DOI: 10.34133/2021/9834163.].

Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.

Introduction: Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution.