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International Journal of Computer Assisted Radiology and Surgery

https://read.qxmd.com/read/38985412/towards-multimodal-graph-neural-networks-for-surgical-instrument-anticipation
#1
JOURNAL ARTICLE
Lars Wagner, Dennis N Schneider, Leon Mayer, Alissa Jell, Carolin Müller, Alexander Lenz, Alois Knoll, Dirk Wilhelm
PURPOSE: Decision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based integration of multimodal data as a basis for decision support systems that can dynamically adapt to the surgical workflow has not yet been established. Therefore, we propose a knowledge-enhanced method for fusing multimodal data for anticipation tasks...
July 10, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38976178/an-intensity-based-self-supervised-domain-adaptation-method-for-intervertebral-disc-segmentation-in-magnetic-resonance-imaging
#2
JOURNAL ARTICLE
Maria Chiara Fiorentino, Francesca Pia Villani, Rafael Benito Herce, Miguel Angel González Ballester, Adriano Mancini, Karen López-Linares Román
BACKGROUND AND OBJECTIVE: Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets. METHODS: The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans...
July 8, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38970745/a-novel-contact-optimization-algorithm-for-endomicroscopic-surface-scanning
#3
JOURNAL ARTICLE
Xingfeng Xu, Shengzhe Zhao, Lun Gong, Siyang Zuo
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) offers real-time, cell-level imaging and holds promise for early cancer diagnosis. However, a large area surface scanning for image acquisition is needed to overcome the limitation of field-of-view. Obtaining high-quality images during scanning requires maintaining a stable contact distance between the tissue and probe. This work presents a novel contact optimization algorithm to acquire high-quality pCLE images. METHODS: The contact optimization algorithm, based on swarm intelligence of whale optimization algorithm, is designed to optimize the probe position, according to the quality of the image acquired by probe...
July 6, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38965166/transformers-for-colorectal-cancer-segmentation-in-ct-imaging
#4
JOURNAL ARTICLE
Georg Hille, Pavan Tummala, Lena Spitz, Sylvia Saalfeld
PURPOSE: Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures...
July 4, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38965165/deep-learning-based-segmentation-of-left-ventricular-myocardium-on-dynamic-contrast-enhanced-mri-a-comprehensive-evaluation-across-temporal-frames
#5
JOURNAL ARTICLE
Raufiya Jafari, Radhakrishan Verma, Vinayak Aggarwal, Rakesh Kumar Gupta, Anup Singh
PURPOSE: Cardiac perfusion MRI is vital for disease diagnosis, treatment planning, and risk stratification, with anomalies serving as markers of underlying ischemic pathologies. AI-assisted methods and tools enable accurate and efficient left ventricular (LV) myocardium segmentation on all DCE-MRI timeframes, offering a solution to the challenges posed by the multidimensional nature of the data. This study aims to develop and assess an automated method for LV myocardial segmentation on DCE-MRI data of a local hospital...
July 4, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38955902/applying-artificial-intelligence-on-eda-sensor-data-to-predict-stress-on-minimally-invasive-robotic-assisted-surgery
#6
JOURNAL ARTICLE
Daniel Caballero, Manuel J Pérez-Salazar, Juan A Sánchez-Margallo, Francisco M Sánchez-Margallo
PURPOSE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity. METHODS: For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels...
July 2, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38951363/generalisation-capabilities-of-machine-learning-algorithms-for-the-detection-of-the-subthalamic-nucleus-in-micro-electrode-recordings
#7
JOURNAL ARTICLE
Thibault Martin, Pierre Jannin, John S H Baxter
PURPOSE: Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms...
July 1, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38942947/a-usability-analysis-of-augmented-reality-and-haptics-for-surgical-planning
#8
JOURNAL ARTICLE
Negar Kazemipour, Amir Hooshiar, Marta Kersten-Oertel
PURPOSE: Proper visualization and interaction with complex anatomical data can improve understanding, allowing for more intuitive surgical planning. The goal of our work was to study what the most intuitive yet practical platforms for interacting with 3D medical data are in the context of surgical planning. METHODS: We compared planning using a monitor and mouse, a monitor with a haptic device, and an augmented reality (AR) head-mounted display which uses a gesture-based interaction...
June 28, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38935246/preliminary-study-of-substantia-nigra-analysis-by-tensorial-feature-extraction
#9
JOURNAL ARTICLE
Hayato Itoh, Masahiro Oda, Shinji Saiki, Koji Kamagata, Wataru Sako, Kei-Ichi Ishikawa, Nobutaka Hattori, Shigeki Aoki, Kensaku Mori
PURPOSE: Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. METHOD: We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition...
June 27, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38918281/background-removal-for-debiasing-computer-aided-cytological-diagnosis
#10
JOURNAL ARTICLE
Keita Takeda, Tomoya Sakai, Eiji Mitate
To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning approach for cell segmentation and background removal without requiring cell annotation. A U-Net-based model was trained to separate cells from the background in an unsupervised manner by leveraging the redundancy of the background and the sparsity of cells in liquid-based cytology (LBC) images. The experimental results demonstrate that the U-Net-based model trained on a small set of cytology images can exclude background features and accurately segment cells...
June 25, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38914722/hierarchical-segmentation-of-surgical-scenes-in-laparoscopy
#11
JOURNAL ARTICLE
Pritesh Mehta, David Owen, Maria Grammatikopoulou, Lucy Culshaw, Karen Kerr, Danail Stoyanov, Imanol Luengo
PURPOSE: Segmentation of surgical scenes may provide valuable information for real-time guidance and post-operative analysis. However, in some surgical video frames there is unavoidable ambiguity, leading to incorrect predictions of class or missed detections. In this work, we propose a novel method that alleviates this problem by introducing a hierarchy and associated hierarchical inference scheme that allows broad anatomical structures to be predicted when fine-grained structures cannot be reliably distinguished...
June 24, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38900372/using-diffusion-models-to-generate-synthetic-labeled-data-for-medical-image-segmentation
#12
JOURNAL ARTICLE
Daniel G Saragih, Atsuhiro Hibi, Pascal N Tyrrell
PURPOSE: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity. METHODS: We trained diffusion models on the HyperKvasir dataset, comprising 1000 images of polyps in the human GI tract from 2008 to 2016...
June 20, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38896406/ire-made-easy-introducing-the-robotic-grid-system-for-multiple-parallel-needle-insertion-in-irreversible-electroporation-treatment
#13
JOURNAL ARTICLE
Girindra Wardhana, Jurgen J Fütterer, Momen Abayazid
PURPOSE: Accurate needle placement is crucial for successful tumor treatment using the irreversible electroporation (IRE) method. Multiple needles are inserted around the tumor, ideally in parallel, to achieve uniform electric field distribution. This paper presents a robot utilizing a grid system to enable multiple needles insertion while maintaining parallelism between them. METHODS: The robotic system has two degrees of freedom, which allow for the adjustment of the grid system to accommodate targeting lesions in various positions...
June 19, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38896405/virtual-reconstruction-of-orbital-defects-using-gaussian-process-morphable-models
#14
JOURNAL ARTICLE
Pieter Vanslambrouck, Jeroen Van Dessel, Constantinus Politis, Robin Willaert, Michel Bila, Yi Sun, Peter Claes
PURPOSE: The conventional method to reconstruct the bone level for orbital defects, which is based on mirroring and manual adaptation, is time-consuming and the accuracy highly depends on the expertise of the clinical engineer. The aim of this study is to propose and evaluate an automated reconstruction method utilizing a Gaussian process morphable model (GPMM). METHODS: Sixty-five Computed Tomography (CT) scans of healthy midfaces were used to create a GPMM that can model shape variations of the orbital region...
June 19, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38890223/characterizing-the-accuracy-of-robotic-bronchoscopy-in-localization-targeting-of-small-pulmonary-lesions
#15
JOURNAL ARTICLE
Jessica Copeland, Mehida Rojas-Alexandre, Lilian Tsai, Franklin King, Nobuhiko Hata
PURPOSE: Considering the recent implementation of lung cancer screening guidelines, it is crucial that small pulmonary nodules are accurately diagnosed. There is a significant need for quick, precise, and minimally invasive biopsy methods, especially for patients with small lung lesions in the outer periphery. Robotic bronchoscopy (RB) has recently emerged as a novel solution. The purpose of this study was to evaluate the accuracy of RB compared to the existing standard, electromagnetic navigational bronchoscopy (EM-NB)...
June 18, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38888820/head-motion-corrected-eye-gaze-tracking-with-the-da-vinci-surgical-system
#16
JOURNAL ARTICLE
Alexandre Banks, Alaa Eldin Abdelaal, Septimiu Salcudean
PURPOSE: To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context. METHODS: A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location...
June 18, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38888819/periacetabular-osteotomy-using-computed-tomography-based-navigation-preoperative-planning-and-accuracy-evaluation
#17
JOURNAL ARTICLE
Yutaka Inaba, Taro Tezuka, Masatoshi Oba, Hyonmin Choe, Hiroyuki Ike
PURPOSE: Since 2011, we have used computed tomography (CT)-based navigation to perform safe and accurate rotational acetabular osteotomy (RAO) for treating developmental dysplasia of the hip. We developed a new method with four fiducial points to improve the accuracy of a published technique. In this study, we introduced a new method to achieve reorientation in accordance with planning and evaluated its accuracy. METHODS: This study included 40 joints, which underwent RAO used CT-based navigation...
June 18, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38884893/autonomous-navigation-of-catheters-and-guidewires-in-mechanical-thrombectomy-using-inverse-reinforcement-learning
#18
JOURNAL ARTICLE
Harry Robertshaw, Lennart Karstensen, Benjamin Jackson, Alejandro Granados, Thomas C Booth
PURPOSE: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations...
June 17, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38884892/model-based-individual-life-spanning-documentation-in-visceral-surgery-a-proof-of-concept
#19
JOURNAL ARTICLE
Maximilian Berlet, Alissa Jell, Lars Wagner, Lukas Bernhard, Jonas Fuchtmann, Luca Wegener, Hubertus Feussner, Helmut Friess, Dirk Wilhelm
INTRODUCTION: Surgical documentation has many implications. However, its primary function is to transfer information about surgical procedures to other medical professionals. Thereby, written reports describing procedures in detail are the current standard, impeding comprehensive understanding of patient-individual life-spanning surgical course, especially if surgeries are performed at a timely distance and in diverse facilities. Therefore, we developed a novel model-based approach for documentation of visceral surgeries, denoted as 'Surgical Documentation Markup-Modeling' (SDM-M)...
June 17, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38879844/brainlossnet-a-fast-accurate-and-robust-method-to-estimate-brain-volume-loss-from-longitudinal-mri
#20
JOURNAL ARTICLE
Roland Opfer, Julia Krüger, Thomas Buddenkotte, Lothar Spies, Finn Behrendt, Sven Schippling, Ralph Buchert
PURPOSE: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based method for BVL-estimation. METHODS: BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans...
June 16, 2024: International Journal of Computer Assisted Radiology and Surgery
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