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Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to a specific organ of interest and often exhibit lower…
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on…
Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are…
We propose a novel technique to register sparse 3D scans in the absence of texture. While existing methods such as KinectFusion or Iterative Closest Points (ICP) heavily rely on dense point clouds, this task is particularly challenging…
A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually,…
Deformable image registration (DIR) is a crucial tool in radiotherapy for analyzing anatomical changes and motion patterns. Current DIR implementations rely on discrete volumetric motion representation, which often leads to compromised…
Image-based, patient-specific modelling of hemodynamics can improve diagnostic capabilities and provide complementary insights to better understand the hemodynamic treatment outcomes. However, computational fluid dynamics simulations remain…
Neuronavigation is widely used in biomedical research and interventions to guide the precise placement of instruments around the head to support procedures such as transcranial magnetic stimulation. Traditional systems, however, rely on…
Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under MRI guidance. CT imaging is used to estimate the acoustic properties that vary…
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is…
Medical image registration is crucial for various clinical and research applications including disease diagnosis or treatment planning which require alignment of images from different modalities, time points, or subjects. Traditional…
We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold with noises. Our approach first constructs a graph representation, referred to as the skeleton, to capture…
Bone surface reconstruction is an essential component of computer-assisted orthopedic surgery(CAOS), forming the foundation for both preoperative planning and intraoperative guidance. Compared to traditional imaging modalities such as…
Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times.…
Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modelity in prostate imaging is essential. However, manual…
Recent advances in imaging techniques have enabled us to visualize lung tumors or nodules in early-stage cancer. However, the positions of nodules can change because of intraoperative lung deflation, and the modeling of…
Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks, especially for accurate…
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures. The artificial DVFs allow training in a fully supervised…
Automatic lesion segmentation on thoracic CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, obtaining a large amount of voxel-level annotations for training segmentation networks is prohibitively…
We propose a new method for fine registering multiple point clouds simultaneously. The approach is characterized by being dense, therefore point clouds are not reduced to pre-selected features in advance. Furthermore, the approach is robust…