The author describes their experiences with 2 loved ones suffering from dementia and the decline of their cognitive abilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.
Simpl Med Ultrasound (2024)
October 2024
Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors.
View Article and Find Full Text PDF- a large-scale dataset of 3D medical shapes for computer vision.
Biomed Tech (Berl)
December 2024
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany.
Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).
View Article and Find Full Text PDFToward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.
J Imaging
December 2024
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).
View Article and Find Full Text PDFIntraoperative patient-specific volumetric reconstruction and 3D visualization for laparoscopic liver surgery.
Healthc Technol Lett
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Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding.
View Article and Find Full Text PDFA real truth in dementia.
Fam Syst Health
December 2024
City University of New York, Queensborough Community College, Center for Tutoring and Academic Support.
The author describes their experiences with 2 loved ones suffering from dementia and the decline of their cognitive abilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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