Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data

Diffusion magnetic resonance imaging (dMRI) is developing into one of the most important non-invasive tools for clinical brain research. This development is supported by a project funded by the German Research Foundation, in which four major obstacles related to dMRI data were addressed: (1) the lack of transferability of dMRI data between clinical sites, (2) the lack of training and label data, (3) the potential of complex diffusion data, and (4) the integration of spherical signals in neural networks to improve accuracy. To overcome the problem of different MRI systems producing slightly varying data, the project developed a method for harmonizing MRI signals. To address the issue of limited ground truth data, a framework was developed to synthesize individual diffusion data and complete datasets based on important diffusion characteristics and statistics. The integration of complex signals, often discarded during acquisition, to improve reconstruction was also explored. Finally, new methods were developed to preserve the spherical character of the diffusion data in the DL model. The resulting methods are intended to improve the usability of diffusion imaging data and to enable the creation of processing pipelines for dMRI data in clinical studies and clinical practice

Migratory Dermal Dendritic Cells Act as Rapid Sensors of Protozoan Parasites

Dendritic cells (DC), including those of the skin, act as sentinels for intruding microorganisms. In the epidermis, DC (termed Langerhans cells, LC) are sessile and screen their microenvironment through occasional movements of their dendrites. The spatio-temporal orchestration of antigen encounter by dermal DC (DDC) is not known. Since these cells are thought to be instrumental in the initiation of immune responses during infection, we investigated their behavior directly within their natural microenvironment using intravital two-photon microscopy. Surprisingly, we found that, under homeostatic conditions, DDC were highly motile, continuously crawling through the interstitial space in a Gαi protein-coupled receptor–dependent manner. However, within minutes after intradermal delivery of the protozoan parasite Leishmania major, DDC became immobile and incorporated multiple parasites into cytosolic vacuoles. Parasite uptake occurred through the extension of long, highly dynamic pseudopods capable of tracking and engulfing parasites. This was then followed by rapid dendrite retraction towards the cell body. DDC were proficient at discriminating between parasites and inert particles, and parasite uptake was independent of the presence of neutrophils. Together, our study has visualized the dynamics and microenvironmental context of parasite encounter by an innate immune cell subset during the initiation of the immune response. Our results uncover a unique migratory tissue surveillance program of DDC that ensures the rapid detection of pathogens

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 ​mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients.

Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling

Diffusion MRI anomaly detection in glioma patients

Abstract Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients’ data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies