International Journal of Computer Assisted Radiology and Surgery

PURPOSE: Online C-arm calibration with a mobile fiducial cage plays an essential role in various image-guided interventions. However, it is challenging to develop a fully automatic approach, which requires not only an accurate detection of fiducial projections but also a robust 2D-3D correspondence establishment. METHODS: We propose a novel approach for online C-arm calibration with a mobile fiducial cage. Specifically, a novel mobile calibration cage embedded with 16 fiducials is designed, where the fiducials are arranged to form 4 line patterns with different cross-ratios...

PURPOSE: To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury. METHODS: We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google's MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2...

PURPOSE: This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsistent, partial annotations across different institutes. Learning a federated model from these distributed, partially labeled, and unlabeled samples is an unexplored problem...

PURPOSE: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves competitive performance with training on limited datasets, while also enhancing model robustness in various surgical scenarios. METHODS: We propose a method that harnesses the strengths of pre-trained Vision Transformers (ViTs) and data efficiency of convolutional neural networks (CNNs)...

PURPOSE: Numerous navigation devices for percutaneous, CT-guided interventions exist and are, due to their advantages, increasingly integrated into the clinical workflow. However, effective training methods to ensure safe usage are still lacking. This study compares the potential of an augmented reality (AR) training application with conventional instructions for the Cube Navigation System (CNS), hypothesizing enhanced training with AR, leading to safer clinical usage. METHODS: An AR-tablet app was developed to train users puncturing with CNS...

PURPOSE: Specialized robotic and surgical tools are increasing the complexity of operating rooms (ORs), requiring elaborate preparation especially when techniques or devices are to be used for the first time. Spatial planning can improve efficiency and identify procedural obstacles ahead of time, but real ORs offer little availability to optimize space utilization. Methods for creating reconstructions of physical setups, i.e., digital twins, are needed to enable immersive spatial planning of such complex environments in virtual reality...

PURPOSE: The utilization of image-guided surgery has demonstrated its ability to improve the precision and safety of minimally invasive surgery (MIS). Non-rigid scene reconstruction is a challenge in image-guided system duo to uniform texture, smoke, and instrument occlusion, etc. METHODS: In this paper, we introduced an algorithm for 3D reconstruction aimed at non-rigid surgery scenes. The proposed method comprises two main components: firstly, the front-end process involves the initial reconstruction of 3D information for deformable soft tissues using embedded deformation graph (EDG) on the basis of dual quaternions, enabling the reconstruction without the need for prior knowledge of the target...

PURPOSE: Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. METHODS: Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection...

PURPOSE: Eye gaze tracking and pupillometry are evolving areas within the field of tele-robotic surgery, particularly in the context of estimating cognitive load (CL). However, this is a recent field, and current solutions for gaze and pupil tracking in robotic surgery require assessment. Considering the necessity of stable pupillometry signals for reliable cognitive load estimation, we compare the accuracy of three eye trackers, including head and console-mounted designs. METHODS: We conducted a user study with the da Vinci Research Kit (dVRK), to compare the three designs...

PURPOSE: We developed a novel augmented fluoroscopy-guided intrathoracic stamping technique for localizing small pulmonary nodules in the hybrid operating room. We conducted an observational study to investigate the feasibility of this technique and retrospectively compared two augmented fluoroscopy-guided approaches: intrathoracic and transbronchial. METHODS: From August 2020 to March 2023, consecutive patients underwent single-stage augmented fluoroscopy-guided localization under general anaesthesia...

PURPOSE: Surgical action triplet recognition is a clinically significant yet challenging task. It provides surgeons with detailed information about surgical scenarios, thereby facilitating clinical decision-making. However, the high similarity among action triplets presents a formidable obstacle to recognition. To enhance accuracy, prior methods necessitated the utilization of larger models, thereby incurring a considerable computational burden. METHODS: We propose a novel framework known as the Lite and Mega Models (LAM)...

PURPOSE: This research endeavors to improve tumor localization in minimally invasive surgeries, a challenging task primarily attributable to the absence of tactile feedback and limited visibility. The conventional solution uses laparoscopic ultrasound (LUS) which has a long learning curve and is operator-dependent. METHODS: The proposed approach involves augmenting LUS images onto laparoscopic images to improve the surgeon's ability to estimate tumor and internal organ anatomy...

PURPOSE: This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice. METHODS: Simulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees...

PURPOSE: Advances in deep learning have resulted in effective models for surgical video analysis; however, these models often fail to generalize across medical centers due to domain shift caused by variations in surgical workflow, camera setups, and patient demographics. Recently, object-centric learning has emerged as a promising approach for improved surgical scene understanding, capturing and disentangling visual and semantic properties of surgical tools and anatomy to improve downstream task performance...

PURPOSE: Understanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require large amounts of annotated training data, containing examples of all relevant object classes, which are rarely available. In this work, we propose a method to combine multiple partially annotated datasets, providing complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets...

PURPOSE: The deep circumflex iliac crest flap (DCIA) is used for the reconstruction of the jaw. For fitting of the transplant by computer-aided planning (CAD), a computerized tomography (CT) of the jaw and the pelvis is necessary. Ready-made cutting guides save a pelvic CT and healthcare resources while maintaining the advantages of the CAD planning. METHODS: A total of 2000 CTs of the pelvis were divided into groups of 500 by sex and age (≤ 45 and > 45 years)...

PURPOSE: Thyroid cancer is one of the most common cancers worldwide, with ultrasound-guided biopsy being the method of choice for its early detection. The accuracy of diagnostics directly depends on the qualifications of the ultrasonographers, whose performance can be enhanced through training with phantoms. The aim of this study is to propose a reproducible methodology for designing a neck phantom for ultrasound training and research from widely available materials and to validate its applicability...

PURPOSE: The healthcare industry has a growing need for realistic modeling and efficient simulation of surgical scenes. With effective models of deformable surgical scenes, clinicians are able to conduct surgical planning and surgery training on scenarios close to real-world cases. However, a significant challenge in achieving such a goal is the scarcity of high-quality soft tissue models with accurate shapes and textures. To address this gap, we present a data-driven framework that leverages emerging neural radiance field technology to enable high-quality surgical reconstruction and explore its application for surgical simulations...

PURPOSE: Obtaining large volumes of medical images, required for deep learning development, can be challenging in rare pathologies. Image augmentation and preprocessing offer viable solutions. This work explores the case of necrotising enterocolitis (NEC), a rare but life-threatening condition affecting premature neonates, with challenging radiological diagnosis. We investigate data augmentation and preprocessing techniques and propose two optimised pipelines for developing reliable computer-aided diagnosis models on a limited NEC dataset...

PURPOSE: Surface reconstructions from laryngoscopic videos have the potential to assist clinicians in diagnosing, quantifying, and monitoring airway diseases using minimally invasive techniques. However, tissue movements and deformations make these reconstructions challenging using conventional pipelines. METHODS: To facilitate such reconstructions, we developed video frame pre-filtering and featureless dense matching steps to enhance the Alicevision Meshroom SfM pipeline...