Imperial College London

Professor Dan Elson

Faculty of MedicineDepartment of Surgery & Cancer

Professor of Surgical Imaging
 
 
 
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Contact

 

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

 
 
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Location

 

415 Bessemer BuildingBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

429 results found

Gkouzionis I, Nazarian S, Kawka M, Darzi A, Patel N, Peters C, Elson Det al., 2022, Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery, Journal of Biomedical Optics, ISSN: 1083-3668

Journal article

Han J, Davids J, Ashrafian H, Darzi A, Elson DS, Sodergren Met al., 2021, A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches, INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, ISSN: 1478-5951

Journal article

Cartucho J, Wang C, Huang B, Elson DS, Darzi A, Giannarou Set al., 2021, An enhanced marker pattern that achieves improved accuracy in surgical tool tracking, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, Pages: 1-9, ISSN: 2168-1163

In computer assisted interventions (CAI), surgical tool tracking is crucial for applications such as surgical navigation, surgical skill assessment, visual servoing, and augmented reality. Tracking of cylindrical surgical tools can be achieved by printing and attaching a marker to their shaft. However, the tracking error of existing cylindrical markers is still in the millimetre range, which is too large for applications such as neurosurgery requiring sub-millimetre accuracy. To achieve tool tracking with sub-millimetre accuracy, we designed an enhanced marker pattern, which is captured on images from a monocular laparoscopic camera. The images are used as input for a tracking method which is described in this paper. Our tracking method was compared to the state-of-the-art, on simulation and ex vivo experiments. This comparison shows that our method outperforms the current state-of-the-art. Our marker achieves a mean absolute error of 0.28 [mm] and 0.45 [°] on ex vivo data, and 0.47 [mm] and 1.46 [°] on simulation. Our tracking method is real-time and runs at 55 frames per second for 720×576 image resolution.

Journal article

Nazarian S, Gkouzionis I, Anandakumar A, Patel N, Elson D, Peters Cet al., 2021, Using Diffuse Reflectance Spectroscopy (DRS) to Identify Tumour and Non-tumour Tissue in Upper Gastrointestinal Specimens, Publisher: OXFORD UNIV PRESS, Pages: 41-41, ISSN: 0007-1323

Conference paper

Perrott C, Patil A, Elson D, Peters Cet al., 2021, Novel Methods of Detecting Tumour Margins in Gastrointestinal Cancer Surgery, Publisher: OXFORD UNIV PRESS, ISSN: 0007-1323

Conference paper

Ahmad OF, Mori Y, Misawa M, Kudo S-E, Anderson JT, Bernal J, Berzin TM, Bisschops R, Byrne MF, Chen P-J, East J, Eelbode T, Elson DS, Gurudu S, Histace A, Karnes WE, Repici A, Singh R, Valdastri P, Wallace MB, Wang P, Stoyanov D, Lovat LBet al., 2021, Establishing key research questions for the implementation of artificial intelligence in colonoscopy - a modified Delphi method., Endoscopy, Vol: 53, Pages: 893-901, ISSN: 0013-726X

Background and Aims Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

Journal article

Teh JJ, Cai W, Kedrzycki M, Thiruchelvam PTR, Elson DS, Leff DRet al., 2021, Magseed-Guided Wide Local Excision During the COVID-19 Pandemic: A Tenable Solution to Barriers in Accessing Elective Breast Cancer Surgery, Publisher: OXFORD UNIV PRESS, ISSN: 0007-1323

Conference paper

Kedrzycki MS, Leiloglou M, Chalau V, Chiarini N, Thiruchelvam PTR, Hadjiminas DJ, Hogben KR, Rashid F, Ramakrishnan R, Darzi AW, Elson DS, Leff DRet al., 2021, ASO visual abstract: the impact of temporal variation in indocyanine green administration on tumor identification during fluorescence-guided breast surgery, Annals of Surgical Oncology, Vol: 28, Pages: 650-651, ISSN: 1068-9265

Journal article

Leiloglou M, Kedrzycki MS, Elson DS, Leff DRet al., 2021, ASO author reflections: towards fluorescence guided tumor identification for precision breast conserving surgery., Annals of Surgical Oncology, ISSN: 1068-9265

Journal article

Kedrzycki MS, Leiloglou M, Chalau V, Chiarini N, Thiruchelvam PTR, Hadjiminas DJ, Hogben KR, Rashid F, Ramakrishnan R, Darzi AW, Elson DS, Leff DRet al., 2021, The impact of temporal variation in indocyanine green administration on tumor identification during fluorescence guided breast surgery., Annals of Surgical Oncology, Vol: 28, Pages: 5617-5625, ISSN: 1068-9265

BACKGROUND: On average, 21% of women in the USA treated with Breast Conserving Surgery (BCS) undergo a second operation because of close positive margins. Tumor identification with fluorescence imaging could improve positive margin rates through demarcating location, size, and invasiveness of tumors. We investigated the technique's diagnostic accuracy in detecting tumors during BCS using intravenous indocyanine green (ICG) and a custom-built fluorescence camera system. METHODS: In this single-center prospective clinical study, 40 recruited BCS patients were sub-categorized into two cohorts. In the first 'enhanced permeability and retention' (EPR) cohort, 0.25 mg/kg ICG was injected ~ 25 min prior to tumor excision, and in the second 'angiography' cohort, ~ 5 min prior to tumor excision. Subsequently, an in-house imaging system was used to image the tumor in situ prior to resection, ex vivo following resection, the resection bed, and during grossing in the histopathology laboratory to compare the technique's diagnostic accuracy between the cohorts. RESULTS: The two cohorts were matched in patient and tumor characteristics. The majority of patients had invasive ductal carcinoma with concomitant ductal carcinoma in situ. Tumor-to-background ratio (TBR) in the angiography cohort was superior to the EPR cohort (TBR = 3.18 ± 1.74 vs 2.10 ± 0.92 respectively, p = 0.023). Tumor detection reached sensitivity and specificity scores of 0.82 and 0.93 for the angiography cohort and 0.66 and 0.90 for the EPR cohort, respectively (p = 0.1051 and p = 0.9099). DISCUSSION: ICG administration timing during the angiography phase compared with the EPR phase improved TBR and diagnostic accuracy. Future work will focus on image pattern analysis and adaptation of the camera system to targeting fluorophores specific to breast cancer.

Journal article

Kedrzycki MS, Leiloglou M, Ashrafian H, Jiwa N, Thiruchelvam PTR, Elson DS, Leff DRet al., 2021, Meta-analysis comparing fluorescence imaging with radioisotope and blue dye-guided sentinel node identification for breast cancer surgery., Annals of Surgical Oncology, Vol: 28, Pages: 3738-3748, ISSN: 1068-9265

INTRODUCTION: Conventional methods for axillary sentinel lymph node biopsy (SLNB) are fraught with complications such as allergic reactions, skin tattooing, radiation, and limitations on infrastructure. A novel technique has been developed for lymphatic mapping utilizing fluorescence imaging. This meta-analysis aims to compare the gold standard blue dye and radioisotope (BD-RI) technique with fluorescence-guided SLNB using indocyanine green (ICG). METHODS: This study was registered with PROSPERO (CRD42019129224). The MEDLINE, EMBASE, Scopus, and Web of Science databases were searched using the Medical Subject Heading (MESH) terms 'Surgery' AND 'Lymph node' AND 'Near infrared fluorescence' AND 'Indocyanine green'. Studies containing raw data on the sentinel node identification rate in breast cancer surgery were included. A heterogeneity test (using Cochran's Q) determined the use of fixed- or random-effects models for pooled odds ratios (OR). RESULTS: Overall, 1748 studies were screened, of which 10 met the inclusion criteria for meta-analysis. ICG was equivalent to radioisotope (RI) at sentinel node identification (OR 2.58, 95% confidence interval [CI] 0.35-19.08, p < 0.05) but superior to blue dye (BD) (OR 9.07, 95% CI 6.73-12.23, p < 0.05). Furthermore, ICG was superior to the gold standard BD-RI technique (OR 4.22, 95% CI 2.17-8.20, p < 0.001). CONCLUSION: Fluorescence imaging for axillary sentinel node identification with ICG is equivalent to the single technique using RI, and superior to the dual technique (RI-BD) and single technique with BD. Hospitals using RI and/or BD could consider changing their practice to ICG given the comparable efficacy and improved safety profile, as well as the lesser burden on hospital infrastructure.

Journal article

Lin J, Clancy NT, Qi J, Hu Y, Tatla T, Stoyanov D, Maier-Hein L, Elson DSet al., 2021, Corrigendum to Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks [Medical Image Analysis 48 (2018) 162-176/2018.06.004]., Medical Image Analysis, Vol: 72, Pages: 1-1, ISSN: 1361-8415

The first version of this article neglected to mention that this work was additionally supported by ERC award 637960. This has now been corrected online. The authors would like to apologise for any inconvenience caused.

Journal article

Leiloglou M, Chalau V, Kedrzycki MS, Thiruchelvam P, Darzi A, Leff DR, Elson DSet al., 2021, Tissue texture extraction in indocyanine green fluorescence imaging for breast-conserving surgery, Journal of Physics D: Applied Physics, Vol: 54, ISSN: 0022-3727

A two-camera fluorescence system for indocyanine green (ICG) signal detection has been developed and tested in a clinical feasibility trial of ten patients, with a resolution in the submillimetre scale. Immediately after systemic ICG injection, the two-camera system can detect ICG signals in vivo (~2.5 mg ${{\text{l}}^{ - 1}}$ or 3.2 × ${10^{ - 6}}{ }$ M). Qualitative assessment has shown that the fluorescence signal does not always correlate with the cancer location in the surgical scene. Conversely, fluorescence image texture metrics when used with the logistic regression model yields good accuracy scores in detecting cancer. We have demonstrated that intraoperative fluorescence imaging for resection guidance is a feasible solution to tackle the current challenge of positive resection margins in breast conserving surgery.

Journal article

Kedrzycki MS, Leiloglou M, Chalau V, Lin J, Thiruchelvam PTR, Elson DS, Leff DRet al., 2021, Guiding light to optimize wide local excisions: the "GLOW" study, Volume XXII 2021 Annual Meeting Scientific Session, Publisher: Springer, Pages: S199-S200, ISSN: 1068-9265

Conference paper

Kedrzycki M, Leiloglou M, Leff D, Elson D, Chalau V, Thiruchelvam P, Darzi Aet al., 2021, Versatility in Fluorescence Guided Surgery with the GLOW Camera System, Surgical Life: The Journal of the Association of Surgeons of Great Britain and Ireland

Journal article

Collins JW, Marcus HJ, Ghazi A, Sridhar A, Hashimoto D, Hager G, Arezzo A, Jannin P, Maier-Hein L, Marz K, Valdastri P, Mori K, Elson D, Giannarou S, Slack M, Hares L, Beaulieu Y, Levy J, Laplante G, Ramadorai A, Jarc A, Andrews B, Garcia P, Neemuchwala H, Andrusaite A, Kimpe T, Hawkes D, Kelly JD, Stoyanov Det al., 2021, Ethical implications of AI in robotic surgical training: A Delphi consensus statement, European Urology Focus, ISSN: 2405-4569

ContextAs the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.ObjectivesTo provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.Evidence acquisitionThe project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.Evidence synthesisThere was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.ConclusionsUsing the Delphi methodology, we achieved international consensus among experts to develop and reach

Journal article

Leiloglou M, Gkouzionis I, Kedrzycki MS, Cartucho J, Vadzim C, Darzi A, Leff DR, Elson DSet al., 2021, Real-time spectral tracking routine for fluorescence hyperspectral guidance in breast conserving surgery

Fast spectral tracking routine, using simultaneous analysis of color and monochrome images, was developed and tested in phantoms. This routine could improve the efficiency of fluorescence hyperspectral imaging for breast conserving surgery guidance.

Conference paper

Huang B, Zheng J-Q, Nguyen A, Tuch D, Vyas K, Giannarou S, Elson DSet al., 2021, Self-supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 227-237, ISSN: 0302-9743

Conference paper

Cartucho J, Tukra S, Li Y, S Elson D, Giannarou Set al., 2021, VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol: 9, Pages: 331-338, ISSN: 2168-1163

Surgical robots rely on robust and efficient computer vision algorithms to be able to intervene in real-time. The main problem, however, is that the training or testing of such algorithms, especially when using deep learning techniques, requires large endoscopic datasets which are challenging to obtain, since they require expensive hardware, ethical approvals, patient consent and access to hospitals. This paper presents VisionBlender, a solution to efficiently generate large and accurate endoscopic datasets for validating surgical vision algorithms. VisionBlender is a synthetic dataset generator that adds a user interface to Blender, allowing users to generate realistic video sequences with ground truth maps of depth, disparity, segmentation masks, surface normals, optical flow, object pose, and camera parameters. VisionBlender was built with special focus on robotic surgery, and examples of endoscopic data that can be generated using this tool are presented. Possible applications are also discussed, and here we present one of those applications where the generated data has been used to train and evaluate state-of-the-art 3D reconstruction algorithms. Being able to generate realistic endoscopic datasets efficiently, VisionBlender promises an exciting step forward in robotic surgery.

Journal article

Kedrzycki MS, Elson DS, Leff DR, 2020, ASO author reflections: fluorescence-guided sentinel node biopsy for breast cancer, Annals of Surgical Oncology, Vol: 28, Pages: 3749-3750, ISSN: 1068-9265

Journal article

Huang B, Tsai Y-Y, Cartucho J, Vyas K, Tuch D, Giannarou S, Elson DSet al., 2020, Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe, International Journal of Computer Assisted Radiology and Surgery, Vol: 15, Pages: 1389-1397, ISSN: 1861-6410

PurposeIn surgical oncology, complete cancer resection and lymph node identification are challenging due to the lack of reliable intraoperative visualization. Recently, endoscopic radio-guided cancer resection has been introduced where a novel tethered laparoscopic gamma detector can be used to determine the location of tracer activity, which can complement preoperative nuclear imaging data and endoscopic imaging. However, these probes do not clearly indicate where on the tissue surface the activity originates, making localization of pathological sites difficult and increasing the mental workload of the surgeons. Therefore, a robust real-time gamma probe tracking system integrated with augmented reality is proposed.MethodsA dual-pattern marker has been attached to the gamma probe, which combines chessboard vertices and circular dots for higher detection accuracy. Both patterns are detected simultaneously based on blob detection and the pixel intensity-based vertices detector and used to estimate the pose of the probe. Temporal information is incorporated into the framework to reduce tracking failure. Furthermore, we utilized the 3D point cloud generated from structure from motion to find the intersection between the probe axis and the tissue surface. When presented as an augmented image, this can provide visual feedback to the surgeons.ResultsThe method has been validated with ground truth probe pose data generated using the OptiTrack system. When detecting the orientation of the pose using circular dots and chessboard dots alone, the mean error obtained is 0.05∘and 0.06∘, respectively. As for the translation, the mean error for each pattern is 1.78 mm and 1.81 mm. The detection limits for pitch, roll and yaw are 360∘,360∘ and 8∘–82∘∪188∘–352∘.ConclusionThe performance evaluation results show that this dual-pattern marker can provide high detection rates, as well as more accurate pose estimation and a larger workspace than the previously proposed hyb

Journal article

Kedrzycki M, Leiloglou M, Ashrafian H, Jiwa N, Thiruchelvam P, Elson D, Leff Det al., 2020, Meta-analysis of Sentinel Node Mapping Techniques Comparing Near-infrared Fluorescence Imaging to Blue Dye and Radioisotope, 21st Annual Meeting of the American-Society-of-Breast-Surgeons (ASBS), Publisher: SPRINGER, Pages: S535-S536, ISSN: 1068-9265

Conference paper

Kedrzycki M, Leiloglou M, Chalau V, Thiruchelvam P, Lin J, Elson D, Leff Det al., 2020, First-in-Human Study Using the 'GLOW' Near Infrared Camera System in Breast Cancer, 21st Annual Meeting of the American-Society-of-Breast-Surgeons (ASBS), Publisher: SPRINGER, Pages: S382-S383, ISSN: 1068-9265

Conference paper

Clancy NT, Jones G, Maier-Hein L, Elson DS, Stoyanov Det al., 2020, Surgical spectral imaging., Medical Image Analysis, Vol: 63, Pages: 1-18, ISSN: 1361-8415

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation.

Journal article

Zhao M, Oude Vrielink TJC, Kogkas A, Runciman M, Elson D, Mylonas Get al., 2020, LaryngoTORS: a novel cable-driven parallel robotic system for transoral laser phonosurgery, IEEE Robotics and Automation Letters, Vol: 5, Pages: 1516-1523, ISSN: 2377-3766

Transoral laser phonosurgery is a commonly used surgical procedure in which a laser beam is used to perform incision, ablation or photocoagulation of laryngeal tissues. Two techniques are commonly practiced: free beam and fiber delivery. For free beam delivery, a laser scanner is integrated into a surgical microscope to provide an accurate laser scanning pattern. This approach can only be used under direct line of sight, which may cause increased postoperative pain to the patient and injury, is uncomfortable for the surgeon during prolonged operations, the manipulability is poor and extensive training is required. In contrast, in the fiber delivery technique, a flexible fiber is used to transmit the laser beam and therefore does not require direct line of sight. However, this can only achieve manual level accuracy, repeatability and velocity, and does not allow for pattern scanning. Robotic systems have been developed to overcome the limitations of both techniques. However, these systems offer limited workspace and degrees-of-freedom (DoF), limiting their clinical applicability. This work presents the LaryngoTORS, a robotic system that aims at overcoming the limitations of the two techniques, by using a cable-driven parallel mechanism (CDPM) attached at the end of a curved laryngeal blade for controlling the end tip of the laser fiber. The system allows autonomous generation of scanning patterns or user driven freepath scanning. Path scan validation demonstrated errors as low as 0.054±0.028 mm and high repeatability of 0.027±0.020 mm (6×2 mm arc line). Ex vivo tests on chicken tissue have been carried out. The results show the ability of the system to overcome limitations of current methods with high accuracy and repeatability using the superior fiber delivery approach.

Journal article

He C, Chang J, He H, Liu S, Elson DS, Ma H, Booth MJet al., 2020, GRIN lens based polarization endoscope – from conception to application, Label-free Biomedical Imaging and Sensing (LBIS) 2020, Publisher: SPIE

Graded index (GRIN) lenses focus light through a radially symmetric refractive index profile. It is not widely appreciated that the ion-exchange process that creates the index profile also causes a radially symmetric birefringence variation. This property is usually considered a nuisance, such that manufacturing processes are optimized to keep it to a minimum. Here, a new Mueller matrix (MM) polarimeter based on a spatially engineered polarization state generating array and GRIN lens cascade for measuring the MM of a region of a sample in a single-shot is presented. We explore using the GRIN lens cascade for a functional analyzer to calculate multiple Stokes vectors and the MM of the target in a snapshot. A designed validation sample is used to test the reliability of this polarimeter. To understand more potential biomedical applications, human breast ductal carcinoma slides at two pathological progression stages are detected by this polarimeter. The MM polar decomposition parameters then can be calculated from the measured MMs, and quantitatively compared with the equivalent data sampled by a MM microscope. The results indicate that the polarimeter and the measured polarization parameters are capable of differentiating the healthy and carcinoma status of human breast tissue efficiently. It has potential to act as a polarization detected fiber-based probe to assist further minimally invasive clinical diagnosis.

Conference paper

Leiloglou M, Gkouzionis I, Avila-Rencoret FB, Chalau V, Kedrzycki M, Darzi A, Leff DR, Elson DSet al., 2020, Snapshot hyperspectral system for breast conserving surgery guidance

There is an unmet need for accurate tumour localization in vivo during breast conserving surgery. Herein a novel snapshot hyperspectral system is presented for accurately detecting the intrinsic fluorescence signal in real-time fluorescence guided surgery.

Conference paper

Elson D, 2020, Multispectral and polarization-resolved endoscopic surgical imaging (invited), SPIE Photonics Europe

Conference paper

Gkouzionis IA, Avila-Rencoret F, Peters C, Elson Det al., 2020, Hyperspectral circumferential resection margin assessment for gastrointestinal cancer surgery, Biophotonics and Imaging Graduate Summer School 2020

Conference paper

Kedrzycki M, Leiloglou M, Leff D, Elson Det al., 2020, Illuminating Cancer: A Systematic Review of Fluorophores available for Fluorescence Guided Surgery in Humans, European Molecular Imaging Meeting

Conference paper

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