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Oleksandra Ivashchenko

Researcher at Netherlands Cancer Institute

Publications -  18
Citations -  154

Oleksandra Ivashchenko is an academic researcher from Netherlands Cancer Institute. The author has contributed to research in topics: Medicine & Image-guided surgery. The author has an hindex of 6, co-authored 12 publications receiving 96 citations. Previous affiliations of Oleksandra Ivashchenko include Leiden University Medical Center.

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Journal ArticleDOI

Quarter-millimeter-resolution molecular mouse imaging with U-SPECT⁺.

TL;DR: U-SPECT+ allows discrimination between molecular concentrations in adjacent volumes of as small as 0.015 mL, which is significantly better than can be imaged by any existing SPECT or PET system, which makes it more and more attractive to replace ex vivo methods and allows monitoring biological processes in tiny parts of organs in vivo.
Book ChapterDOI

MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net.

TL;DR: A workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery is developed using a reduced 3D U-Net for segmentation, followed by non- Rigid coherent point drift (CPD) registration.
Journal ArticleDOI

Prospective study on image-guided navigation surgery for pelvic malignancies

TL;DR: This study explores the application of a novel electromagnetic navigation system to guide pelvic surgery in patients with advanced tumors and lymph nodes in the pelvis.
Journal ArticleDOI

Artificial intelligence in the medical physics community: An international survey.

TL;DR: In this paper, a web-based survey was conducted to assess current perceptions, practices and education needs pertaining to artificial intelligence (AI) in the medical physics field, which included questions about education, personal knowledge, needs, research and professionalism around AI in medical physics.
Journal ArticleDOI

A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images.

TL;DR: A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed, which enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitate better perception of organ’s anatomy during preparation and execution of liver surgeries.