ArticleInternational journal of computer assisted radiology and surgery2019
Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review.Cancer medicine · 2023Pooled it
- Placenta segmentation redefined: review of deep learning integration of magnetic resonance imaging and ultrasound imaging.Visual computing for industry, biomedicine, and art · 2025Review
- Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.BMC medical imaging · 2025Article
- Pilot study of an arterial enhancement fraction-based model for progression prediction in HCC undergoing chemoembolization.Frontiers in oncology · 2025Article
- A review of lightweight convolutional neural networks for ultrasound signal classification.Frontiers in physiology · 2025Review
- Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images.BMC medical imaging · 2024Article
- Pretrained subtraction and segmentation model for coronary angiograms.Scientific reports · 2024Article
- Metal implant segmentation in CT images based on diffusion model.BMC medical imaging · 2024Article
- A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.BMC medical imaging · 2024Article
- Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction.BMC medical imaging · 2024Article
- Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.IEEE journal of biomedical and health informatics · 2023Article
- An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction.Biomedical signal processing and control · 2023Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
objectivesSurgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments.
methodsThis paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process. RESULTS AND
conclusionThe proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.