Photoacoustic Quantification of Tissue Oxygenation Using Conditional Invertible Neural Networks.

Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation.

Correction: Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/44204.].

Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.

Background: The COVID-19 pandemic is characterized by rapid increases in infection burden owing to the emergence of new variants with higher transmissibility and immune escape. To date, monitoring the COVID-19 pandemic has mainly relied on passive surveillance, yielding biased epidemiological measures owing to the disproportionate number of undetected asymptomatic cases. Active surveillance could provide accurate estimates of the true prevalence to forecast the evolution of the pandemic, enabling evidence-based decision-making.

Beyond rankings: Learning (more) from algorithm validation.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail.

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method's current lack of robustness and generalizability.

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic segmentation based on spectral imaging data and obtained during open surgery has received almost no attention to date. To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs.

Robust hand tracking for surgical telestration.

Purpose: As human failure has been shown to be one primary cause for post-operative death, surgical training is of the utmost socioeconomic importance. In this context, the concept of surgical telestration has been introduced to enable experienced surgeons to efficiently and effectively mentor trainees in an intuitive way. While previous approaches to telestration have concentrated on overlaying drawings on surgical videos, we explore the augmented reality (AR) visualization of surgical hands to imitate the direct interaction with the situs.

Learned spectral decoloring enables photoacoustic oximetry.

The ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO[Formula: see text], enables a wide variety of possible applications. sO[Formula: see text] can be computed from the ratio of oxyhemoglobin HbO[Formula: see text] and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO[Formula: see text] yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal.

Light source calibration for multispectral imaging in surgery.

Purpose: Live intra-operative functional imaging has multiple potential clinical applications, such as localization of ischemia, assessment of organ transplantation success and perfusion monitoring. Recent research has shown that live monitoring of functional tissue properties, such as tissue oxygenation and blood volume fraction, is possible using multispectral imaging in laparoscopic surgery. While the illuminant spectrum is typically kept constant in laparoscopic surgery and can thus be estimated from preoperative calibration images, a key challenge in open surgery originates from the dynamic changes of lighting conditions.