Imperial College London
Alternate

Professor Daniel Elson

Faculty of Medicine, Department of Surgery & Cancer

Professor of Surgical Imaging
 
 
 
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Contact

 

+44 (0)20 7594 1700daniel.elson Website CV

 
 
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Location

 

415 Bessemer BuildingBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gkouzionis:2023:10.1007/s11548-023-02944-9,
author = {Gkouzionis, I and Zhong, Y and Nazarian, S and Darzi, A and Patel, N and Peters, CJ and Elson, DS},
doi = {10.1007/s11548-023-02944-9},
journal = {International Journal of Computer Assisted Radiology and Surgery},
title = {A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery},
url = {http://dx.doi.org/10.1007/s11548-023-02944-9},
volume = {19},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Gkouzionis,I
AU  - Zhong,Y
AU  - Nazarian,S
AU  - Darzi,A
AU  - Patel,N
AU  - Peters,CJ
AU  - Elson,DS
DO  - 10.1007/s11548-023-02944-9
PY  - 2023///
SN  - 1861-6410
TI  - A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery
T2  - International Journal of Computer Assisted Radiology and Surgery
UR  - http://dx.doi.org/10.1007/s11548-023-02944-9
UR  - https://www.ncbi.nlm.nih.gov/pubmed/37289279
UR  - http://hdl.handle.net/10044/1/104946
VL  - 19
ER  -
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