SignificancePancreatic surgery is a highly demanding and routinely applied procedure for the treatment of several pancreatic lesions. The outcome of patients with malignant entities crucially depends on the margin resection status of the tumor. Frozen section analysis for intraoperative evaluation of tissue is still time consuming and laborious.AimWe describe the application of fiber-based attenuated total reflection infrared (ATR IR) spectroscopy for label-free discrimination of normal pancreatic, tumorous, and pancreatitis tissue. A pilot study for the intraoperative application was performed.ApproachThe method was applied for unprocessed freshly resected tissue samples of 58 patients, and a classification model for differentiating between the distinct tissue classes was established.ResultsThe developed three-class classification model for tissue spectra allows for the delineation of tumors from normal and pancreatitis tissues using a probability score for class assignment. Subsequently, the method was translated into intraoperative application. Fiber optic ATR IR spectra were obtained from freshly resected pancreatic tissue directly in the operating room.ConclusionOur study shows the possibility of applying fiber-based ATR IR spectroscopy in combination with a supervised classification model for rapid pancreatic tissue identification with a high potential for transfer into intraoperative surgical diagnostics.
Pancreatic surgery is a highly demanding and routinely applied procedure for the treatment of several pancreatic lesions. The outcome of patients with malignant entities crucially depends on the margin resection status of the tumor. In this study we describe the application of fiber-based attenuated total reflection infrared (ATR IR) spectroscopy for label-free discrimination of normal pancreatic, tumorous and pancreatitis tissue. The method was applied for the unprocessed freshly resected tissue samples of 40 patients, and a classification model for differentiating between the distinct tissue classes was established. The developed three-class classification model for tissue spectra allows the delineation of tumors from normal and pancreatitis tissues. The classification algorithm provides probability values for each sample to be assigned to normal, tumor or pancreatitis classes. The established probability values were transferred to a Red-Green-Blue (RGB) color plot. Subsequently, the method was translated into intraoperative application. Fiber optic ATR IR spectra were obtained from freshly resected pancreatic tissue directly in the operating room. The spectroscopic findings could subsequently be confirmed by the histology gold standard. This study shows the possibility of applying fiber-based ATR IR spectroscopy in combination with a supervised classification model for rapid pancreatic tissue identification with a high potential for transfer into intraoperative surgical diagnostics.
Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples.Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications.
Computer-Assisted Surgery (CAS) aids the surgeon by enriching the surgical scene with additional information in order to improve patient outcome. One such aid may be the superimposition of important structures (such as blood vessels and tumors) over a laparoscopic image stream. In liver surgery, this may be achieved by creating a dense map of the abdominal environment surrounding the liver, registering a preoperative model (CT scan) to the liver within this map, and tracking the relative pose of the camera. Thereby, known structures may be rendered into images from the camera perspective. This intraoperative map of the scene may be constructed, and the relative pose of the laparoscope camera estimated, using Simultaneous Localisation and Mapping (SLAM). The intraoperative scene poses unique challenges, such as: homogeneous surface textures, sparse visual features, specular reflections and camera motions specific to laparoscopy. This work compares the efficacies of two state-of the-art SLAM systems in the context of laparoscopic surgery, on a newly collected phantom dataset with ground truth trajectory and surface data. The SLAM systems chosen contrast strongly in implementation: one sparse and feature-based, ORB-SLAM3,1{3 and one dense and featureless, ElasticFusion.4 We find that ORB-SLAM3 greatly outperforms ElasticFusion in trajectory estimation and is more stable on sequences from laparoscopic surgeries. However, when extended to give a dense output, ORB-SLAM3 performs surface reconstruction comparably to ElasticFusion. Our evaluation of these systems serves as a basis for expanding the use of SLAM algorithms in the context of laparoscopic liver surgery and Minimally Invasive Surgery (MIS) more generally.
Providing the surgeon with the right assistance at the right time during minimally-invasive surgery requires computer-assisted surgery systems to perceive and understand the current surgical scene. This can be achieved by analyzing the endoscopic image stream. However, endoscopic images often contain artifacts, such as specular highlights, which can hinder further processing steps, e.g., stereo reconstruction, image segmentation, and visual instrument tracking. Hence, correcting them is a necessary preprocessing step. In this paper, we propose a machine learning approach for automatic specular highlight removal from a single endoscopic image. We train a residual convolutional neural network (CNN) to localize and remove specular highlights in endoscopic images using weakly labeled data. The labels merely indicate whether an image does or does not contain a specular highlight. To train the CNN, we employ a generative adversarial network (GAN), which introduces an adversary to judge the performance of the CNN during training. We extend this approach by (1) adding a self-regularization loss to reduce image modification in non-specular areas and by (2) including a further network to automatically generate paired training data from which the CNN can learn. A comparative evaluation shows that our approach outperforms model-based methods for specular highlight removal in endoscopic images.
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