Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions, despite often suffering from artifacts that pose challenges for accurate interpretation. While the degraded image quality can affect image analysis, the availability of high quality, preoperative scans offers potential for improvements. Here we consider a setting where preoperative CT and intraoperative CBCT scans are available, however, the alignment (registration) between the scans is imperfect to simulate a real world scenario. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect on segmentation performance. For this experiment we use synthetically generated data containing real CT and synthetic CBCT volumes with corresponding voxel annotations. We show that this fusion setup improves segmentation performance in $18$ out of $20$ investigated setups.
From Data Completeness to Data Sufficiency: A Task-Driven Imaging Framework for Intraoperative CBCT under Quality-Time-Dose Trade-offs
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Shanghai First-Imaging Information Technology Co., LTD.（上海第一影像信息技术有限公司）
National Center for Tumor Diseases（国家肿瘤疾病中心）；University Hospital Carl-Gustav-Carus（卡尔·古斯塔夫·卡鲁斯大学医院）；TU Dresden（德累斯顿工业大学）
Non-rigid registration is a key component in soft-tissue navigation. We focus on laparoscopic liver surgery, where we register the organ model obtained from a preoperative CT scan to the intraoperative partial organ surface, reconstructed from the laparoscopic video. This is a challenging task due to sparse and noisy intraoperative data, real-time requirements and many unknowns - such as tissue properties and boundary conditions. Furthermore, establishing correspondences between pre- and intraoperative data can be extremely difficult since the liver usually lacks distinct surface features and …
3D Reconstruction from Arthroscopic Images using NeRF: a preliminary in-silico study
LaTIM - UMR 1101 INSERM（INSERM LaTIM联合研究单位）；University of Western Brittany（布列塔尼大学）；IMT Atlantique（大西洋理工大学）；Brest University Hospital（布雷斯特大学医院）；Universidad de Alcala（阿尔卡拉大学）；INSA Centre Val de Loire（卢瓦尔河谷国立应用科学学院）；Rennes University Hospital（雷恩大学医院）
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology（哈佛-麻省理工健康科学与技术, 麻省理工学院）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（计算机科学与人工智能实验室, 麻省理工学院）；Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Saint Luke’s Marion Bloch Neuroscience Institute（圣路易斯马里恩布洛克神经科学研究所）；Department of Critical Care Medicine, Shriners Children’s Hospital（谢尔曼儿童医院重症医学科）；Department of Interventional Neuroradiology, Boston Children’s Hospital（波士顿儿童医院介入神经放射科）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（阿提努拉A·马丁诺斯生物医学成像中心, 麻省总医院）
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
Carnegie Vanguard High School（卡内基先锋高中）；Devon Preparatory High School（德文预备高中）；Lake Travis High School（特拉维斯湖高中）；Irvington High School（欧文顿高中）；Algoverse AI Research（Algoverse AI研究机构）
During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image format…
Periacetabular osteotomy is a challenging surgical procedure for treating developmental hip dysplasia, providing greater coverage of the femoral head via relocation of a patient's acetabulum. Since fluoroscopic imaging is frequently used in the surgical workflow, computer-assisted X-Ray navigation of osteotomes and the relocated acetabular fragment should be feasible. We use intensity-based 2D/3D registration to estimate the pelvis pose with respect to fluoroscopic images, recover relative poses of multiple views, and triangulate landmarks which may be used for navigation. Existing similarity …
Z 2 Sky Technologies Inc.（Z 2 天工科技公司）；University of Rochester（罗切斯特大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所 中国科学院智能信息处理重点实验室）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advance Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention. However, CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects, causing vertebra segmentation, even manually, a demanding task. In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations. To overco…
Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery (MISS) and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from dis…
Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡大学先进光学技术研究生院）；Siemens Healthcare GmbH（西门子医疗有限公司）
The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-…
Correctly capturing intraoperative brain shift in image-guided neurosurgical procedures is a critical task for aligning preoperative data with intraoperative geometry for ensuring accurate surgical navigation. While the finite element method (FEM) is a proven technique to effectively approximate soft tissue deformation through biomechanical formulations, their degree of success boils down to a trade-off between accuracy and speed. To circumvent this problem, the most recent works in this domain have proposed leveraging data-driven models obtained by training various machine learning algorithms…
University College London（伦敦大学学院）；University of Oxford（牛津大学）；Siemens Healthineers（西门子医疗）；King’s College London（伦敦国王学院）
Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detaile…
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
LASIGE, Faculty of Sciences, University of Lisbon（里斯本大学科学学院LASIGE）；Department of Neurosurgery and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布里洛妇女医院神经外科与放射科）；Gina Cody School of Engineering and Computer Science, Concordia University（康科迪亚大学工程与计算机科学学院）；Cancer Research UK Cambridge Centre, University of Cambridge（剑桥大学癌症研究英国中心）；Department of Oncology, University of Cambridge（剑桥大学肿瘤科）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）；Computational Health Informatics Program (CHIP) and Department of Radiology, Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院计算健康信息学计划与放射科）；Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM（索邦大学巴黎脑研究所-ICM）
