World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5219
World Ranking
13054
National Ranking
835

Overview

Martin Rajchl is affiliated with Imperial College London in the United Kingdom. Their research primarily focuses on the field of Medicine, with specific studies in Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Genetics, and Cancer Research.

Their work covers several main research topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced MRI Techniques and Applications
  • Cardiac Imaging and Diagnostics
  • Advanced X-ray and CT Imaging
  • Glioma Diagnosis and Treatment
  • Cancer Genomics and Diagnostics

Martin Rajchl has contributed to several recent publications, addressing different aspects of medical imaging and cancer research. Notable papers include:

  • Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net), 2020, Medical Physics
  • Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma, 2024, GigaScience
  • EPCO-15. LINKING HISTOLOGICAL GLIOBLASTOMA PHENOTYPES TO TRANSCRIPTIONAL SUBTYPES AND PROGNOSIS USING DEEP LEARNING, 2022, Neuro-Oncology

Throughout their research career, Rajchl has frequently collaborated with several coauthors. Frequent coauthors include:

  • Thomas Roetzer-Pejrimovsky
  • Karl-Heinz Nenning
  • Barbara Kiesel
  • Johanna Klughammer
  • Bernhard Baumann

Their work has appeared in multiple publication venues, reflecting their interdisciplinary approach in medical imaging and cancer research. Key venues of publication are:

  • Medical Physics
  • GigaScience
  • Neuro-Oncology

Rajchl's research integrates advanced machine learning techniques to address challenges in medical image segmentation, glioma diagnosis, and linking histological phenotypes to transcriptional subtypes. Their studies contribute to understanding and improving diagnostic accuracy and prognostic evaluation in oncology and cardiology.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven G. McDonagh

  • DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

    Martin Rajchl;Matthew C. H. Lee;Ozan Oktay;Konstantinos Kamnitsas

  • Semi-supervised learning for network-based cardiac MR image segmentation

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • Metric learning with spectral graph convolutions on brain connectivity networks.

    Sofia Ira Ktena;Sarah Parisot;Enzo Ferrante;Martin Rajchl

  • MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

    Adriënne M. Mendrik;Koen L. Vincken;Hugo J. Kuijf;Marcel Breeuwer

  • Right ventricle segmentation from cardiac MRI: a collation study.

    Caroline Petitjean;Maria A. Zuluaga;Wenjia Bai;Jean Nicolas Dacher

  • Distance metric learning using graph convolutional networks: application to functional brain networks

    Sofia Ira Ktena;Sarah Parisot;Enzo Ferrante;Martin Rajchl

  • Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI

    Vanya V. Valindria;Nick Pawlowski;Martin Rajchl;Ioannis Lavdas

  • Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

    Wu Qiu;Jing Yuan;Eranga Ukwatta;Yue Sun

  • Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders

    Nick Pawlowski;Matthew C.H. Lee;Martin Rajchl;Steven McDonagh

  • Implicit Weight Uncertainty in Neural Networks.

    Nick Pawlowski;Martin Rajchl;Ben Glocker

  • DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

    Nick Pawlowski;Sofia Ira Ktena;Matthew C. H. Lee;Bernhard Kainz

  • Left ventricle segmentation in MRI via convex relaxed distribution matching

    Cyrus M.S. Nambakhsh;Jing Yuan;Kumaradevan Punithakumar;Aashish Goela

  • Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

    Ozan Oktay;Wenjia Bai;Ricardo Guerrero;Martin Rajchl

  • Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

    Amir Alansary;Konstantinos Kamnitsas;Alice Davidson;Rostislav Khlebnikov

  • Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

    Martin Rajchl;Jing Yuan;James A. White;Eranga Ukwatta

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven McDonagh

  • Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling

    Carlo Biffi;Ozan Oktay;Giacomo Tarroni;Wenjia Bai

  • Dual optimization based prostate zonal segmentation in 3D MR images.

    Wu Qiu;Jing Yuan;Eranga Ukwatta;Yue Sun

  • 3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces

    E. Ukwatta;Jing Yuan;M. Rajchl;Wu Qiu

Frequent Co-Authors

Terry M. Peters
Terry M. Peters University of Western Ontario
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Aaron Fenster
Aaron Fenster University of Western Ontario
Ben Glocker
Ben Glocker Imperial College London
Wenjia Bai
Wenjia Bai Imperial College London
Ozan Oktay
Ozan Oktay Imperial College London
Bernhard Kainz
Bernhard Kainz Imperial College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Paul M. Matthews
Paul M. Matthews Imperial College London
Joseph V. Hajnal
Joseph V. Hajnal King's College London

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