Imperial College London


Faculty of EngineeringDepartment of Computing

Honorary Research Fellow



m.rajchl Website CV




Huxley BuildingSouth Kensington Campus





I am an Imperial College Research Fellow at the Depts. of Computing and Medicine and founder of the Deep Learning Toolkit (DLTK) for medical imaging.

DLTK website source model zoo


More resources:

Twitter link

Google Scholar link

Personal Github link

BioMedIA Github link

Advanced Segmentation Tools (ASETS) website .



Zabihollahy F, Rajchl M, White JA, et al., 2020, Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net), Medical Physics, Vol:47, ISSN:0094-2405, Pages:1645-1655

Meng Q, Zimmer V, Hou B, et al., 2019, Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging, Ieee Transactions on Medical Imaging, Vol:38, ISSN:0278-0062, Pages:2755-2767


Roetzer-Pejrimovsky T, Kiesel B, Nenning K-H, et al., 2022, LINKING HISTOLOGICAL GLIOBLASTOMA PHENOTYPES TO TRANSCRIPTIONAL SUBTYPES AND PROGNOSIS USING DEEP LEARNING, 27th Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO), OXFORD UNIV PRESS INC, Pages:118-119, ISSN:1522-8517

Wang C, Rajchl M, Chan ADC, et al., 2019, An ensemble of U-Net architecture variants for left atrial segmentation, Conference on Medical Imaging - Computer-Aided Diagnosis, SPIE-INT SOC OPTICAL ENGINEERING, ISSN:0277-786X

Biffi C, Oktay O, Tarroni G, et al., 2018, Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling, International Conference On Medical Image Computing & Computer Assisted Intervention, Springer, Pages:464-471, ISSN:0302-9743

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