Fluorescence angiography classification in colorectal surgery -- A preliminary report

• URL: http://arxiv.org/abs/2206.05935v1
• Date: Mon, 13 Jun 2022 07:10:59 GMT
• Title: Fluorescence angiography classification in colorectal surgery -- A preliminary report
• Authors: Antonio S Soares, Sophia Bano, Neil T Clancy, Laurence B Lovat, Danail Stoyanov and Manish Chand
• Abstract summary: The aim is to develop an artificial intelligence algorithm to classify colonic tissue as 'perfused' or 'not perfused' based on fluorescence angiography data. A web based app was made available to deploy the algorithm.
• Score: 8.075715438276244
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Background: Fluorescence angiography has shown very promising results in reducing anastomotic leaks by allowing the surgeon to select optimally perfused tissue. However, subjective interpretation of the fluorescent signal still hinders broad application of the technique, as significant variation between different surgeons exists. Our aim is to develop an artificial intelligence algorithm to classify colonic tissue as 'perfused' or 'not perfused' based on intraoperative fluorescence angiography data. Methods: A classification model with a Resnet architecture was trained on a dataset of fluorescence angiography videos of colorectal resections at a tertiary referral centre. Frames corresponding to fluorescent and non-fluorescent segments of colon were used to train a classification algorithm. Validation using frames from patients not used in the training set was performed, including both data collected using the same equipment and data collected using a different camera. Performance metrics were calculated, and saliency maps used to further analyse the output. A decision boundary was identified based on the tissue classification. Results: A convolutional neural network was successfully trained on 1790 frames from 7 patients and validated in 24 frames from 14 patients. The accuracy on the training set was 100%, on the validation set was 80%. Recall and precision were respectively 100% and 100% on the training set and 68.8% and 91.7% on the validation set. Conclusion: Automated classification of intraoperative fluorescence angiography with a high degree of accuracy is possible and allows automated decision boundary identification. This will enable surgeons to standardise the technique of fluorescence angiography. A web based app was made available to deploy the algorithm.

Related papers

• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning [0.6749750044497731]
  Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas.
  arXiv  Detail & Related papers  (2024-02-01T13:43:33Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Identifying Visible Tissue in Intraoperative Ultrasound Images during Brain Surgery: A Method and Application [1.4408275800058263]
  Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe. We propose a method for the identification of the visible tissue, which enables the analysis of ultrasound probe and tissue contact.
  arXiv  Detail & Related papers  (2023-06-01T23:06:14Z)
• Semi-supervised GAN for Bladder Tissue Classification in Multi-Domain Endoscopic Images [10.48945682277992]
  We propose a semi-surprised Generative Adrial Network (GAN)-based method composed of three main components. The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively.
  arXiv  Detail & Related papers  (2022-12-21T21:32:36Z)
• Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants [58.720142291102135]
  This study develops an algorithm for calculating bone growth in contact with metallic implants. Bone and implant tissue were manually segmented in the training data set. In terms of network accuracy, the model reached around 98%.
  arXiv  Detail & Related papers  (2022-04-22T08:17:20Z)
• Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology [9.037868656840736]
  In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
  arXiv  Detail & Related papers  (2022-04-18T13:31:53Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures [83.48996461770017]
  This work presents a Deep Learning method based on the late fusion of different convolutional architectures. We built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
  arXiv  Detail & Related papers  (2020-12-23T14:38:35Z)
• Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images [10.01138352319106]
  Five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray images. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models.
  arXiv  Detail & Related papers  (2020-06-03T22:55:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.