Cognitive Vision in Robotic Surgery

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• Journal article
  Weld A, Dixon L, Anichini G, Patel N, Nimer A, Dyck M, O'Neill K, Lim A, Giannarou S, Camp Set al., 2024,

  Challenges with segmenting intraoperative ultrasound for brain tumours

  , Acta Neurochirurgica: the European journal of neurosurgery, Vol: 166, ISSN: 0001-6268

  Objective - Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods - 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist's, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons [ I o U : 0.789 , D S C : 0.876 , H D : 103.227 ] and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [ I o U : 0.723 , D S C : 0.813 , H D : 115.675 ] . After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [ I o U : 37 % , D S C : 41 % , H D : 54 % ] . Conclusion - This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncologi

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• Journal article
  Lo FP-W, Qiu J, Wang Z, Chen J, Xiao B, Yuan W, Giannarou S, Frost G, Lo Bet al., 2024,

  Dietary Assessment With Multimodal ChatGPT: A Systematic Analysis

  , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 28, Pages: 7577-7587, ISSN: 2168-2194
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• Journal article
  Xu C, Xu H, Giannarou S, 2024,

  Distance Regression Enhanced With Temporal Information Fusion and Adversarial Training for Robot-Assisted Endomicroscopy

  , IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 43, Pages: 3895-3908, ISSN: 0278-0062
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• Journal article
  You J, Ajlouni S, Kakaletri I, Charalampaki P, Giannarou Set al., 2024,

  XRelevanceCAM: towards explainable tissue characterization with improved localisation of pathological structures in probe-based confocal laser endomicroscopy

  , INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, Vol: 19, Pages: 1061-1073, ISSN: 1861-6410
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• Journal article
  Tukra S, Xu H, Xu C, Giannarou Set al., 2024,

  Generalizable stereo depth estimation with masked image modelling

  , Healthcare Technology Letters, Vol: 11, Pages: 108-116, ISSN: 2053-3713

  Generalizable and accurate stereo depth estimation is vital for 3D reconstruction, especially in surgery. Supervised learning methods obtain best performance however, limited ground truth data for surgical scenes limits generalizability. Self-supervised methods don't need ground truth, but suffer from scale ambiguity and incorrect disparity prediction due to inconsistency of photometric loss. This work proposes a two-phase training procedure that is generalizable and retains the high performance of supervised methods. It entails: (1) performing self-supervised representation learning of left and right views via masked image modelling (MIM) to learn generalizable semantic stereo features (2) utilizing the MIM pre-trained model to learn robust depth representation via supervised learning for disparity estimation on synthetic data only. To improve stereo representations learnt via MIM, perceptual loss terms are introduced, which improve the model's stereo representations learnt by explicitly encouraging the learning of higher scene-level features. Qualitative and quantitative performance evaluation on surgical and natural scenes shows that the approach achieves sub-millimetre accuracy and lowest errors respectively, setting a new state-of-the-art. Despite not training on surgical nor natural scene data for disparity estimation.

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• Journal article
  Dyck M, Weld A, Klodmann J, Kirst A, Dixon L, Anichini G, Camp S, Albu-Schaeffer A, Giannarou Set al., 2024,

  Toward Safe and Collaborative Robotic Ultrasound Tissue Scanning in Neurosurgery

  , IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, Vol: 6, Pages: 64-67
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• Book chapter
  Giannarou S, Xu C, Roddan A, 2024,

  Endomicroscopy

  , Biophotonics and Biosensing: from Fundamental Research to Clinical Trials through Advances of Signal and Image Processing, Pages: 269-284

  Endomicroscopy is an enabling technology that can transform tissue characterization, allowing optical biopsies to be taken during diagnostic and interventional procedures, assisting tissue characterization and decision-making. New techniques, such as probe-based confocal laser endomicroscopy (pCLE) have enabled direct visualization of the tissue at a microscopic level and have been approved for clinical use in a range of clinical applications. Recent pilot studies suggest that the technique may have a role in identifying residual cancer tissue and improving resection rates. The aim of this chapter is to present the technological advances in this area, describe the challenges and limitations associated with this imaging modality and present methods which have been developed to facilitate the application of this technique as well as understanding of the collected data.

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• Journal article
  Cartucho J, Weld A, Tukra S, Xu H, Matsuzaki H, Ishikawa T, Kwon M, Jang YE, Kim K-J, Lee G, Bai B, Kahrs LA, Boecking L, Allmendinger S, Mueller L, Zhang Y, Jin Y, Bano S, Vasconcelos F, Reiter W, Hajek J, Silva B, Lima E, Vilaca JL, Queiros S, Giannarou Set al., 2024,

  SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery

  , MEDICAL IMAGE ANALYSIS, Vol: 91, ISSN: 1361-8415
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• Conference paper
  Roddan A, Yu Z, Leiloglou M, Chalau V, Anichini G, Giannarou S, Elson Det al., 2024,

  Towards real-time hyperspectral imaging in neurosurgery

  , Conference on Clinical Biophotonics III, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
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• Conference paper
  Roddan A, Xu C, Ajlouni S, Kakaletri I, Charalampaki P, Giannarou Set al., 2023,

  Explainable image classification with improved trustworthiness for tissue characterisation

  , MICCAI 2023, Publisher: Springer Nature Switzerland, Pages: 575-585, ISSN: 0302-9743

  The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model’s attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. The proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.

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