Publications by authors named "Anant Vemuri"

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.

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Multispectral imaging provides valuable information on tissue composition such as hemoglobin oxygen saturation. However, the real-time application of this technique in interventional medicine can be challenging due to the long acquisition times needed for large amounts of hyperspectral data with hundreds of bands. While this challenge can partially be addressed by choosing a discriminative subset of bands, the band selection methods proposed to date are mainly restricted by the availability of often hard to obtain reference measurements.

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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.

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Article Synopsis
  • Optical imaging is emerging as a vital technique for sensing in operating rooms, utilizing machine learning to translate multispectral reflectance data into tissue parameters like oxygenation, but challenges remain regarding the ill-posed nature of this problem.
  • A new framework based on invertible neural networks evaluates optical imaging systems by mapping multispectral data to full posterior probability distributions, enhancing the understanding of uncertainties in tissue parameter inference.
  • The findings indicate that while tissue oxygenation estimation is reliable, blood volume fraction may have ambiguities, with increased spectral bands potentially improving clarity in results.
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Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data.

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Purpose: Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue.

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Barrett's oesophagus, a premalignant condition of the oesophagus has been on a rise in the recent years. The standard diagnostic protocol for Barrett's involves obtaining biopsies at suspicious regions along the oesophagus. The localization and tracking of these biopsy sites "interoperatively" poses a significant challenge for providing targeted treatments and tracking disease progression.

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The screening of oesophageal adenocarcinoma involves obtaining biopsies at different regions along the oesophagus. The localization and tracking of these biopsy sites inter-operatively poses a significant challenge for providing targeted treatments. This paper presents a novel framework for providing a guided navigation to the gastro-intestinal specialist for accurate re-positioning of the endoscope at previously targeted sites.

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Background: Surgical procedures have undergone considerable advancement during the last few decades. More recently, the availability of some imaging methods intraoperatively has added a new dimension to minimally invasive techniques. Augmented reality in surgery has been a topic of intense interest and research.

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