Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods

Heterogeneous ensemble of classifiers for sub-cellular image classification based on local ternary patterns

In this chapter we make an extensive study of different state-of-the-art classifiers for building an heterogeneous ensemble for sub-cellular image classification. As features for representing each image we used local ternary patterns. Our aim is to show that it is possible to boost the performance of a stand-alone texture descriptor (here we use the high performance method named local ternary patterns) by an heterogeneous ensemble. First, we compare different classification approaches (different kind of boosting; SVM with various kernels; diverse recent ensemble of decision trees.) in five datasets; then, we show that an heterogeneous ensemble, based on the fusion of different classifiers, performs consistently well across all the tested datasets. The most important result is showing that some very recent approaches and our proposed ensemble outperform also SVM classifier (the well known and widely used LibSVM implementation), even when both kernel selection and the various SVM parameters are carefully tuned. Finally we validated our ensemble also using several datasets from the UCI Repository and other standard pattern classification problems. The Matlab code of the classifiers used in the proposed ensemble is available at bias.csr.unibo.it/nanni/HET.rar. © 2014 Springer-Verlag Berlin Heidelberg

Limits to anatomical accuracy of diffusion tractography using modern approaches.

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain