3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts

3D convolutional neural networks for brain tumor segmentation: a comparison of multi-resolution architectures

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55524-9 15Peer Reviewe

3D convolutional neural networks for brain tumor segmentation

This paper presents our work on applying 3D Convolutional Networks for brain tumor segmentation for the BRATS challenge. We are currently experimenting with different 3D fully convolutional architectures. We present preliminary results using these architectures and outline our future steps and experiments, which involve hyperparameter optimization, comparison of the models’ performance and implementation of a post-processing stage to eliminate false positive predictions.Peer ReviewedPostprint (published version

3D convolutional neural networks for brain tumor segmentation

This paper presents our work on applying 3D Convolutional Networks for brain tumor segmentation for the BRATS challenge. We are currently experimenting with different 3D fully convolutional architectures. We present preliminary results using these architectures and outline our future steps and experiments, which involve hyperparameter optimization, comparison of the models’ performance and implementation of a post-processing stage to eliminate false positive predictions.Peer Reviewe

NeAT: a nonlinear analysis toolbox for neuroimaging

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer's disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). Adrià Casamitjana is supported by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU Research Fellowship. Juan D. Gispert holds a “‘Ramón y Cajal’” fellowship (RYC-2013-13054)

Cross-vendor and cross-protocol harmonisation of diffusion MRI data: A comparative study

We present a comparison of five different methods that estimate mappings between scanners for diffusion MRI data harmonisation. The methods are evaluated on a dedicated dataset of the same subjects acquired on three distinct scanners with ‘standard’ and ‘state-of-the-art’ protocols, with the latter having higher spatial and angular resolution. Our results show that cross-vendor harmonisation and spatial/angular resolution enhancement of single-shell diffusion data sets can be performed reliably, although some challenges remain. The dataset is available upon request and can serve as a useful testbed for future method development in cross-site/cross-hardware and cross-vendor diffusion MRI harmonisation

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more timeconsuming, Cartesian-grid scheme. Importantly, we show that simple pre-and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods

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

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset