179 research outputs found
An attempt at beating the 3D U-Net
The U-Net is arguably the most successful segmentation architecture in the
medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor
Segmentation Challenge and attempt to improve upon it by augmenting it with
residual and pre-activation residual blocks. Cross-validation results on the
training cases suggest only very minor, barely measurable improvements. Due to
marginally higher dice scores, the residual 3D U-Net is chosen for test set
prediction. With a Composite Dice score of 91.23 on the test set, our method
outperformed all 105 competing teams and won the KiTS2019 challenge by a small
margin
Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans
We participate in the AutoPET II challenge by modifying nnU-Net only through
its easy to understand and modify 'nnUNetPlans.json' file. By switching to a
UNet with residual encoder, increasing the batch size and increasing the patch
size we obtain a configuration that substantially outperforms the automatically
configured nnU-Net baseline (5-fold cross-validation Dice score of 65.14 vs
33.28) at the expense of increased compute requirements for model training. Our
final submission ensembles the two most promising configurations. At the time
of submission our method ranks first on the preliminary test set
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Many medical imaging techniques utilize fitting approaches for quantitative
parameter estimation and analysis. Common examples are pharmacokinetic modeling
in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and
Z-spectra analysis in chemical exchange saturation transfer MRI. Most available
software tools are limited to a special purpose and do not allow for own
developments and extensions. Furthermore, they are mostly designed as
stand-alone solutions using external frameworks and thus cannot be easily
incorporated natively in the analysis workflow. We present a framework for
medical image fitting tasks that is included in MITK, following a rigorous
open-source, well-integrated and operating system independent policy. Software
engineering-wise, the local models, the fitting infrastructure and the results
representation are abstracted and thus can be easily adapted to any model
fitting task on image data, independent of image modality or model. Several
ready-to-use libraries for model fitting and use-cases, including fit
evaluation and visualization, were implemented. Their embedding into MITK
allows for easy data loading, pre- and post-processing and thus a natural
inclusion of model fitting into an overarching workflow. As an example, we
present a comprehensive set of plug-ins for the analysis of DCE MRI data, which
we validated on existing and novel digital phantoms, yielding competitive
deviations between fit and ground truth. Providing a very flexible environment,
our software mainly addresses developers of medical imaging software that
includes model fitting algorithms and tools. Additionally, the framework is of
high interest to users in the domain of perfusion MRI, as it offers
feature-rich, freely available, validated tools to perform pharmacokinetic
analysis on DCE MRI data, with both interactive and automatized batch
processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi
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