The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- Bjoern H Menze, A. Jakab, K. Leemput
- Computer ScienceIEEE Transactions on Medical Imaging
- 1 October 2015
The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
- Amber L. Simpson, M. Antonelli, M. Jorge Cardoso
- Computer ScienceArXiv
- 25 February 2019
A large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain.
The Liver Tumor Segmentation Benchmark (LiTS)
- Patrick Bilic, P. Christ, Bjoern H Menze
- Medicine, Computer ScienceMedical Image Anal.
- 13 January 2019
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
- P. Christ, M. Elshaer, Bjoern H Menze
- Computer Science, MedicineInternational Conference on Medical Image…
- 7 October 2016
The results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over \(94\,\%\) for liver with computation times below 100 s per volume.
ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
- O. Maier, Bjoern H Menze, Jia-Hong Lee
- Medicine, Computer ScienceMedical Image Anal.
- 2017
Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations
- E. Konukoglu, O. Clatz, N. Ayache
- MathematicsIEEE Transactions on Medical Imaging
- 2010
A parameter estimation method for reaction-diffusion tumor growth models using time series of medical images and it is shown that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells.
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
- P. Christ, Florian Ettlinger, Bjoern H Menze
- Computer Science, MedicineArXiv
- 20 February 2017
Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume.
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images
- Ezequiel Geremia, O. Clatz, Bjoern H Menze, E. Konukoglu, A. Criminisi, N. Ayache
- Computer ScienceNeuroImage
- 15 July 2011
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
- Bjoern H Menze, B. Kelm, F. Hamprecht
- Computer ScienceBMC Bioinformatics
- 1 July 2009
The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random Forest classifier, in spite of their limitation to model linear dependencies only.
On Oblique Random Forests
- Bjoern H Menze, B. Kelm, D. Splitthoff, U. Köthe, F. Hamprecht
- Computer ScienceECML/PKDD
- 5 September 2011
This work proposes to employ "oblique" random forests (oRF) built from multivariate trees which explicitly learn optimal split directions at internal nodes using linear discriminative models, rather than using random coefficients as the original oRF.
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