Automated Real Time Delineation of Supraclavicular Brachial Plexus in Neck Ultrasonography Videos: A Deep Learning Approach

• URL: http://arxiv.org/abs/2308.03717v1
• Date: Mon, 7 Aug 2023 16:40:19 GMT
• Title: Automated Real Time Delineation of Supraclavicular Brachial Plexus in Neck Ultrasonography Videos: A Deep Learning Approach
• Authors: Abhay Tyagi, Abhishek Tyagi, Manpreet Kaur, Jayanthi Sivaswami, Richa Aggarwal, Kapil Dev Soni, Anjan Trikha
• Abstract summary: This study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Deep learning models can be leveraged for real time segmentation of supraclavicular brachial plexus in neck ultrasonography videos with high accuracy and reliability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed from separate US scanners with and without fine-tuning. The results demonstrate that deep learning models can be leveraged for real time segmentation of supraclavicular brachial plexus in neck ultrasonography videos with high accuracy and reliability. Model was also tested for its ability to differentiate between supraclavicular and adjoining interscalene brachial plexus. The entire dataset has been released publicly for further study by the research community.

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)
• FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
  Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training. We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue. We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch. This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
  arXiv  Detail & Related papers  (2024-03-18T19:13:02Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion [48.52403516006036]
  This paper proposes a vision-based approach allowing autonomous robotic US limb scanning. To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories. In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs.
  arXiv  Detail & Related papers  (2022-08-10T15:39:20Z)
• Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation [10.18353060771133]
  We develop a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We show that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%.
  arXiv  Detail & Related papers  (2022-05-17T07:23:28Z)
• A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images [0.0]
  We propose a reliable and time efficient machine-learning approach to track muscle-tendon junctions in ultrasound videos. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems. Our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position.
  arXiv  Detail & Related papers  (2022-02-10T18:02:46Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• Towards Robotic Knee Arthroscopy: Multi-Scale Network for Tissue-Tool Segmentation [0.0]
  We present a densely connected shape aware multi-scale segmentation model which captures multi-scale features and integrates shape features to achieve tissue-tool segmentations. With the publicly available polyp dataset our proposed model achieved 5.09 % accuracy improvement.
  arXiv  Detail & Related papers  (2021-10-06T11:20:01Z)
• The interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions [3.0969191504482247]
  The purpose of this study is to differentiate malignant and benign lesions by using endobronchial ultrasound (EBUS) image. Our model is robust to noise and able to fuse various imaging features and aspiration of EBUS videos.
  arXiv  Detail & Related papers  (2021-07-29T08:38:17Z)
• Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery [10.562627972607892]
  We show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images.
  arXiv  Detail & Related papers  (2020-07-17T16:33:33Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.