Medical instrument detection in ultrasound: a review

Yang, Haixiu; Shan, Caifeng; Kolen, Alexander F.; With, Peter H. N. de

doi:10.1007/s10462-022-10287-1

Cited by 10 publications

(8 citation statements)

References 114 publications

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“…There are several valuable review articles discussing the use of CNNs in various medical applications, including the diagnosis of COVID-19 [63], counting microorganisms [64,65], image captioning [66], detection of medical instruments in ultrasound images [67], classification of breast cancer [68], analysis of CT and PET images [69], and transfer learning in medical image processing [70].…”

Section: Application Of Deep Convolutional Neural Network (Dcnns)mentioning

confidence: 99%

Deep Neural Networks and Applications in Medical Research

Abut,

Okut,

Zackula

et al. 2024

Artificial Intelligence

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Artificial Intelligence (AI) has played a significant role in improving decision-making within the healthcare system. AI includes machine learning, which encompasses a subset called artificial neural networks (ANNs). These networks mimic how biological neurons in the brain signal one another. In this chapter, we conduct a seminal review of ANNs and explain how prediction and classification tasks can be conducted in the field of medicine. Basic information is provided showing how neural networks solve the problem of determining disease subsets by analyzing huge amounts of structured and unstructured patient data. We also provide information on the application of conventional ANNs and deep convolutional neural networks (DCNNs) that are specific to medical image processing. For example, DCNNs can be used to detect the edges of an item within an image. The acquired knowledge can then be transferred so that similar edges can be identified on another image. This chapter is unique; it is specifically aimed at medical professionals who are interested in artificial intelligence. Because we will demonstrate the application in a straightforward manner, researchers from other technical fields will also benefit.

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Section: Application Of Deep Convolutional Neural Network (Dcnns)mentioning

confidence: 99%

Deep Neural Networks and Applications in Medical Research

Abut,

Okut,

Zackula

et al. 2024

Artificial Intelligence

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“…TEE-probe pose estimation through 2D/3D registration methods based on iterative refinement such as Direct Splat Correlation (DSC) and Patch Gradient Correlation (PCG) have been implemented [5]. Instrument pose estimation from 3D ultrasound data volumes has received substantially more attention [35], [36].…”

Section: B Object Pose Estimation In X-raymentioning

confidence: 99%

Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

Viviers,

Filatova,

Termeer

et al. 2024

IEEE Trans. on Image Process.

Self Cite

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Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive simulations, whilst lacking real-time computation. We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image. The proposed YOLOv5-6D pose model achieves competitive results on public benchmarks whilst being considerably faster at 42 FPS on GPU. In addition, the method generalizes across varying X-ray acquisition geometry and semantic image complexity to enable accurate pose estimation over different domains. Finally, the proposed approach is tested for bone-screw pose estimation for computer-aided guidance during spine surgeries. The model achieves a 92.41% by the 0.1•d ADD-S metric, demonstrating a promising approach for enhancing surgical precision and patient outcomes. The code for YOLOv5-6D is publicly available at https://github.com/cviviers/YOLOv5-6D-Pose.

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“…A general machine learning integration workflow mainly involves three steps; data input and preprocessing, model inference and post-processing [2]. The data input step was fully handled by the Clarius API.…”

Section: A Integrationmentioning

confidence: 99%

“…These aim at either localizing or segmenting the needle within an ultrasound image. Of the two tasks, needle segmentation is key since it derives a more precise result with each pixel in the image assigned one of two categories i.e needle or background [2]. Deep learning-based methods have gained prominence in segmentation tasks in tandem with the progress made in the field of computer vision [2].…”

Section: Introductionmentioning

confidence: 99%

“…Of the two tasks, needle segmentation is key since it derives a more precise result with each pixel in the image assigned one of two categories i.e needle or background [2]. Deep learning-based methods have gained prominence in segmentation tasks in tandem with the progress made in the field of computer vision [2]. Several approaches have been proposed for needle segmenta-tion in 3D ultrasound, for instance, Pourtaherian et al [11] proposed an approach that accurately detects short needles in 3D ultrasound.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems

Okwija¹,

Nabacwa²,

Imanirakiza³

et al. 2022

Preprint

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Background Accurate needle placement is crucial during minimally invasive procedures such as biopsies, regional anesthesia, and fluid aspiration. 2D Ultrasound is widely used for needle guidance during such procedures, however, it has a limited field-of-view and poor needle visibility for steep insertion angles. Methods In this work, we propose a novel machine learning (ML)-based method for real-time needle segmentation in handheld 2D ultrasound systems. The proposed method involves a fast and simple annotation technique allowing for the labeling of large datasets. It then utilizes the U-Net architecture which is modified to allow for easy integration into a handheld ultrasound system. Two datasets were used in this work, one consisting of B-mode ultrasound videos obtained from human tissue and the other consisting of videos and frames from chicken, porcine and bovine tissue. The model is trained on 1262 frames and evaluated on 209 frames. Results This approach achieves an Intersection Over Union (IoU) of 0.75 and a dice coefficient of 0.851 on frames obtained from human tissue. The model is integrated into the processing pipeline of a portable ultrasound system and achieves an overall processing speed of about 8 frames per second. The proposed approach outperforms state-of-the-art methods for needle segmentation while achieving real-time integration. This work is a step forward towards real-time needle guidance using machine learning-based algorithms in handheld ultrasound systems.

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Medical instrument detection in ultrasound: a review

Cited by 10 publications

References 114 publications

Deep Neural Networks and Applications in Medical Research

Deep Neural Networks and Applications in Medical Research

Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems

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