A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery

PURPOSE: A positive circumferential resection margin (CRM) for oesophageal and gastric carcinoma is associated with local recurrence and poorer long-term survival. Diffuse reflectance spectroscopy (DRS) is a non-invasive technology able to distinguish tissue type based on spectral data. The aim of this study was to develop a deep learning-based method for DRS probe detection and tracking to aid classification of tumour and non-tumour gastrointestinal (GI) tissue in real time. METHODS: Data collected from both ex vivo human tissue specimen and sold tissue phantoms were used for the training and retrospective validation of the developed neural network framework. Specifically, a neural network based on the You Only Look Once (YOLO) v5 network was developed to accurately detect and track the tip of the DRS probe on video data acquired during an ex vivo clinical study. RESULTS: Different metrics were used to analyse the performance of the proposed probe detection and tracking framework, such as precision, recall, mAP 0.5, and Euclidean distance. Overall, the developed framework achieved a 93% precision at 23 FPS for probe detection, while the average Euclidean distance error was 4.90 pixels. CONCLUSION: The use of a deep learning approach for markerless DRS probe detection and tracking system could pave the way for real-time classification of GI tissue to aid margin assessment in cancer resection surgery and has potential to be applied in routine surgical practice

Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery

Significance: Diffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements. Aim: This study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue. Approach: A green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis. Results: The measured root mean square error (RMSE) of DRS probe tip tracking was 1.18  ±  0.58  mm and 1.05  ±  0.28  mm for the x and y dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus. Conclusions: We have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery

Using diffuse reflectance spectroscopy (DRS) to identify tumour and non-tumour tissue in upper gastrointestinal specimens

Aim Cancers of the upper gastrointestinal (GI) tract remain a major contributor to the global cancer risk. Surgery aims to completely resect tumour with clear margins, whilst preserving as much surrounding tissue. Diffuse reflectance spectroscopy (DRS) is a novel technique that presents a promising advancement in cancer diagnosis. We have developed a novel DRS system with tracking capability. Our aim is to classify tumour and non-tumour GI tissue in real-time using this device to aid intra-operative analysis of resection margins. Method An ex-vivo study was undertaken in which data was collected from consecutive patients undergoing upper GI cancer resection surgery between August 2020- January 2021. A hand-held DRS probe and tracking system was used on normal and cancerous tissue to obtain spectral information. After acquisition of all spectra, a classification system using histopathology results was created. A user interface was developed using Python 3.6 and Qt5. A support vector machine was used to classify the results. Results The data included 4974 normal spectra and 2108 tumour spectra. The overall accuracy of the DRS probe in differentiating normal versus tumour tissue was 88.08% for the stomach (sensitivity 84.8%, specificity 89.3%), and 91.42% for the oesophagus (sensitivity 76.3%, specificity 98.9%). Conclusion We have developed a successful DRS system with tracking capability, able to process thousands of spectra in a small timeframe, which can be used in real-time to distinguish tumour and non-tumour tissue. This can be used for intra-operative decision-making during upper GI cancer surgery to help select the best resection plane