clDice -- a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.Comment: * The authors Suprosanna Shit and Johannes C. Paetzold contributed equally to the wor

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

Anatomically informed multi-level fiber tractography for targeted virtual dissection

Objectives: Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. Methods: Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. Results: The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: p= 0.21 for the left and p= 0.53 for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: p< 0.01. Discussion: MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies

Anatomically informed multi-level fiber tractography for improved sensitivity of white matter bundle reconstruction in diffusion MRI

Neurosurgery planning is an important application of fiber tractography which requires the results to be consistent and accurate. Deterministic tractography methods are generally characterized by high specificity and limited sensitivity, whereas the opposite typically holds for probabilistic methods. Here, we propose a multi-level fiber tractography strategy that takes fiber branching into account and incorporates an anatomical prior to provide a balance between true and false positive reconstructions. We evaluated our approach on the MASSIVE dataset and compared its performance to the existing state of art. (Abstract #0855

Anatomically informed multi-level fiber tractography for improved sensitivity of white matter bundle reconstruction in diffusion MRI

Neurosurgery planning is an important application of fiber tractography which requires the results to be consistent and accurate. Deterministic tractography methods are generally characterized by high specificity and limited sensitivity, whereas the opposite typically holds for probabilistic methods. Here, we propose a multi-level fiber tractography strategy that takes fiber branching into account and incorporates an anatomical prior to provide a balance between true and false positive reconstructions. We evaluated our approach on the MASSIVE dataset and compared its performance to the existing state of art. (Abstract #0855

Multi-level fiber tracking: evaluation on clinical data

Conventional deterministic fiber tractography approaches commonly used in clinical applications are prone to generating false-negative reconstructions, which might influence further decision-making related to treatment and repeated surgery in patients with brain tumors. Surgery-related effects, such as blood inflow into white matter and edema, further distort the diffusion signal, complicating the task of tractography. We evaluated a novel multi-level fiber tractography approach on data of subjects who had undergone tumor resection. A comparison with conventional deterministic approaches is performed. The results were correlated with the reported motor-function deficit grades. ( Abstract #1746

Multi-level fiber tracking: evaluation on clinical data

Conventional deterministic fiber tractography approaches commonly used in clinical applications are prone to generating false-negative reconstructions, which might influence further decision-making related to treatment and repeated surgery in patients with brain tumors. Surgery-related effects, such as blood inflow into white matter and edema, further distort the diffusion signal, complicating the task of tractography. We evaluated a novel multi-level fiber tractography approach on data of subjects who had undergone tumor resection. A comparison with conventional deterministic approaches is performed. The results were correlated with the reported motor-function deficit grades. ( Abstract #1746