Publications by authors named "Jakub Ceranka"
Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery.
Research Question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos.
Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI.
Comput Methods Programs Biomed
December 2023
The confident detection of metastatic bone disease is essential to improve patients' comfort and increase life expectancy. Multi-parametric magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease, allowing for comprehensive and holistic evaluation of the total tumour volume and treatment response assessment. The major challenges of radiological reading of whole-body MRI come from the amount of data to be reviewed and the scattered distribution of metastases, often of complex shapes.
View Article and Find Full Text PDFTo test and compare different intensity standardization approaches for whole-body multi-parametric MR images, aiming to compensate voxel intensity differences between scans. These differences, common for magnetic resonance imaging, pose problems in image quantification, assessment of changes between a baseline and follow-up scan, and hinder performance of image processing and machine learning algorithms.In this work, we present a comparison on the accuracy of intensity standardization approaches with increasing complexity, for intra- and inter-patient multi-parametric whole-body MRI.
View Article and Find Full Text PDFArticle Synopsis
- Dynamic computer tomography (CT) is being developed for analyzing joint movement at the bone level, but it typically requires manual work to segment bones and identify key landmarks.
- This study introduces an automated workflow that uses a multi-atlas segmentation and landmark propagation framework to efficiently extract bone structures and track joint motion in dynamic CT images.
- The method was tested on CT scans from 15 healthy subjects, achieving high segmentation accuracy (up to 0.94) and showing that the automated motion estimation closely aligns with expert evaluations, suggesting it's a reliable tool for assessing joint kinematics in clinical settings.
Objectives: Multicenter oncology trials increasingly include MRI examinations with apparent diffusion coefficient (ADC) quantification for lesion characterization and follow-up. However, the repeatability and reproducibility (R&R) limits above which a true change in ADC can be considered relevant are poorly defined. This study assessed these limits in a standardized whole-body (WB)-MRI protocol.
View Article and Find Full Text PDFPurpose: To improve multi-atlas segmentation of the skeleton from whole-body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation.
Methods: An improved atlas registration scheme is proposed.
Purpose: To test and compare different registration approaches for performing whole-body diffusion-weighted (wbDWI) image station mosaicing, and its alignment to corresponding anatomical T whole-body image.
Methods: Four different registration strategies aiming at mosaicing of diffusion-weighted image stations, and their alignment to the corresponding whole-body anatomical image, were proposed and evaluated. These included two-step approaches, where diffusion-weighted stations are first combined in a pairwise (Strategy 1) or groupwise (Strategy 2) manner and later non-rigidly aligned to the anatomical image; a direct pairwise mapping of DWI stations onto the anatomical image (Strategy 3); and simultaneous mosaicing of DWI and alignment to the anatomical image (Strategy 4).