Dr Bartek Papiez
Rutherford Fund Fellow at Health Data Research UK
Motion is a massive barrier in cancer imaging hampering developments in cancer research and treatment. My research work has comprised the development of accurate, thus complex and realistic but still computationally efficient, models of organ motion, and has established a solid foundation to reliable quantitative cancer image analysis. The developed image analysis framework stands as an essential tool:
1) for monitoring changes of lung tumours during treatment, especially when a tumour is located close to chest boundaries, preserving discontinuities (i.e. sliding motion) when multiple organs move independently; 2) to enable meaningful analysis of a wide range of dynamic contrast-enhanced imaging sequences (e.g. dynamic contrast-enhanced Magnetic Resonance Imaging, perfusion Computed Tomography), opening to clinical researchers a new opportunity for reliable tumour heterogeneity assessment, leading to a better understanding of functional tumour microenvironment (e.g. tumour metabolism), with possible improvements in the future to patient stratification; 3) for analysis of ex-vivo lung imaging data, exploring metastatic cancer colonies in small rodents, significantly expanding the usability of an in vivo microscopy techniques by reducing the high rate of abortive experiments caused by motion artefacts.
Bartłomiej (Bartek) W. Papież, graduated in Electrical Engineering from the AGH University of Science and Technology in Kraków (Poland) in 2009. He completed a PhD at the University of Central Lancashire in 2012, and subsequently joined the Biomedical Image Analysis Laboratory at the University of Oxford. Between 2012 and 2017, he worked as a post-doctoral research fellow at the Oxford Cancer Imaging Centre focusing on cancer image analysis. In 2013, he was awarded a prestigious Young Scientist Award by the Medical Image Computing and Computer Assisted Intervention Society. In 2015, he was elected to an EPA Cephalosporin Junior Research Fellow at Linacre College. He is a retained lecturer in Engineering Science at Exeter College and Lady Margaret Hall. In 2018, he was awarded Rutherford Fund Fellowship at Health Data Research UK at the Big Data Institute in Oxford.
A full list of publications: Google Scholar page
Follow me: @bwpapiez
Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with Attention
Bourigault E. et al, (2022), Lecture Notes in Computer Science, 189 - 201
D-Net: An AI model for aligning volumes, developed for cartilage segmentation from CT scans contrasted with a type II collagen binding peptide containing di-iodotyrosine
Zheng JQ. et al, (2021), INTERNATIONAL JOURNAL OF EXPERIMENTAL PATHOLOGY, 102, 134 - 135
D-Net: Siamese based Network with Mutual Attention for Volume Alignment.
Zheng J-Q. et al, (2021), CoRR, abs/2101.10248
Deep Learning Classification of Cardiomegaly Using Combined Imaging and Non-imaging ICU Data.
Grant D. et al, (2021), MIUA, 12722, 547 - 558
Medical Image Understanding and Analysis - 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, Proceedings
(2021), MIUA, 12722