Related papers: Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound

Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound

URL: http://arxiv.org/abs/2405.09959v1
Date: Thu, 16 May 2024 10:07:30 GMT
Title: Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
Authors: Reuben Dorent, Erickson Torio, Nazim Haouchine, Colin Galvin, Sarah Frisken, Alexandra Golby, Tina Kapur, William Wells,
Abstract summary: Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. But its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS.
Score: 35.526097492693864
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: \url{https://github.com/ReubenDo/MHVAE-Seg}.

Related papers

VISAGE: Video Synthesis using Action Graphs for Surgery [34.21344214645662]
We introduce the novel task of future video generation in laparoscopic surgery. Our proposed method, VISAGE, leverages the power of action scene graphs to capture the sequential nature of laparoscopic procedures. Results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures.
arXiv Detail & Related papers (2024-10-23T10:28:17Z)
Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom [9.41936397281689]
Improved surgical skill is generally associated with improved patient outcomes, but assessment is subjective and labour-intensive. A new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. A Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill.
arXiv Detail & Related papers (2024-09-25T15:27:44Z)
EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting [53.38166294158047]
EndoGSLAM is an efficient approach for endoscopic surgeries, which integrates streamlined representation and differentiable Gaussianization. Experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches.
arXiv Detail & Related papers (2024-03-22T11:27:43Z)
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)
Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery [50.3022015601057]
We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video. We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets. Our results demonstrate the superiority of our approach compared to unstructured alternatives.
arXiv Detail & Related papers (2024-02-03T00:58:05Z)
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)
Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks [48.732863591145964]
We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
arXiv Detail & Related papers (2021-10-12T21:22:24Z)
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods [15.833413083110903]
This paper presents ESAD, the first large-scale dataset designed to tackle the problem of surgeon action detection in endoscopic minimally invasive surgery. The dataset provides bounding box annotation for 21 action classes on real endoscopic video frames captured during prostatectomy, and was used as the basis of a recent MIDL 2020 challenge.
arXiv Detail & Related papers (2021-04-07T15:11:51Z)

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