Regensburg Medical Image Computing (ReMIC)
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Aims
Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI).
Methods
401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images.
Results
EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793.
Conclusions
To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.
Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.
Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand.
Aims:
The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task).
Methods:
An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D).
Results:
The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed.
Conclusions:
AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.
Objectives: C-11-methionine (MET) is particularly useful in brain tumor diagnosis but unspecific uptake e.g. in cerebral ischemia has been reported (1). The F-18-labeled amino acid O-(2-[F-18]fluoroethyl)-L-tyrosine (FET) shows a similar clinical potential as MET in brain tumor diagnosis but is applicable on a wider clinical scale. The aim of this study was to evaluate the uptake of FET and H-3-MET in focal cortical ischemia in rats by dual tracer autoradiography.
Methods: Focal cortical ischemia was induced in 12 Fisher CDF rats using the photothrombosis model (PT). One day (n=3) , two days (n=5) and 7 days (n=4) after induction of the lesion FET and H-3-MET were injected intravenously. One hour after tracer injection animals were killed, the brains were removed immediately and frozen in 2-methylbutane at -50°C. Brains were cut in coronal sections (thickness: 20 µm) and exposed first to H-3 insensitive photoimager plates to measure FET distribution. After decay of F-18 the distribution of H-3-MET was determined. The autoradiograms were evaluated by regions of interest (ROIs) placed on areas with increased tracer uptake in the PT and the contralateral brain. Lesion to brain ratios (L/B) were calculated by dividing the mean uptake in the lesion and the brain. Based on previous studies in gliomas a L/B ratio > 1.6 was considered as pathological for FET.
Results: Variable increased uptake of both tracers was observed in the PT and its demarcation zone at all stages after PT. The cut-off level of 1.6 for FET was exceeded in 9/12 animals. One day after PT the L/B ratios were 2.0 ± 0.6 for FET vs. 2.1 ± 1.0 for MET (mean ± SD); two days after lesion 2.2 ± 0.7 for FET vs. 2.7 ± 1.0 for MET and 7 days after lesion 2.4 ± 0.4 for FET vs. 2.4 ± 0.1 for MET. In single cases discrepancies in the uptake pattern of FET and MET were observed.
Conclusions: FET like MET may exhibit significant uptake in infarcted areas or the immediate vincinity which has to be considered in the differential diagnosis of unkown brain lesions. The discrepancies in the uptake pattern of FET and MET in some cases indicates either differences in the transport mechanisms of both amino acids or a different affinity for certain cellular components.