Brain tractography involves mapping diffusion-weighted images (DWI) onto streamlines representing neural fibre bundles. Recent research avenues have framed tractography into a reinforcement learning (RL) framework with actor-critic models. However, previous RL-based methods may compromise geometrical relations between the input (DWI) and output (tractogram). More specifically, 3D rotations applied to the input of RL-based tractography are not adequately reflected in the output, indicating a lack of SO(3) equivariance. This study aims to restore the equivariance present in previous non-learning-based methods (e.g., iFOD2 from MRtrix3) to RL-based tractography. To achieve this, we introduce SO(3) equivariant and invariant components for the actors (direction prediction model) and critics (Q-value prediction model), respectively. We employ an SE(3)-equivariant transformer as the next direction prediction function. The fact that both the input DWI and the output directional update can be represented as spherical signals that transform under representations of SO(3) makes this formulation a natural fit for the present problem. The contribution of this work is twofold. First, we discuss rotational equivariance in streamline tractography on a theoretical level. Second, we propose a method that combines RL-based tractography with a rotationally equivariant model. We evaluate the equivariance of the proposed method both locally and globally with phantom and in vivo data. The results show that the proposed method restores the equivariance of Track-to-Learn, which is the state-of-the-art for RL-based tractography. Our code is available at https://github.com/minnelab/SO3TrackToLearn.

Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or disease progression while also considering the individual brain's current status and unique characteristics. While current deep generative models can produce high-resolution anatomically accurate templates for population-wide studies, their ability to predict future aging trajectories for individuals remains limited, particularly in capturing subject-specific neuroanatomical variations over time. In this study, we introduce Individualized Brain Synthesis (InBrainSyn), a framework for synthesizing high-resolution subject-specific longitudinal MRI scans that simulate neurodegeneration in both Alzheimer's disease (AD) and normal aging. InBrainSyn uses a parallel transport algorithm to adapt the population-level aging trajectories learned by a generative deep template network, enabling individualized aging synthesis. As InBrainSyn uses diffeomorphic transformations to simulate aging, the synthesized images are topologically consistent with the original anatomy by design. We evaluated InBrainSyn both quantitatively and qualitatively on AD and healthy control cohorts from the Open Access Series of Imaging Studies - version 3 dataset. Experimentally, InBrainSyn can also model neuroanatomical transitions between normal aging and AD. An evaluation of an external set supports its generalizability. Overall, with only a single baseline scan, InBrainSyn synthesizes realistic 3D spatiotemporal T1w MRI scans, producing personalized longitudinal aging trajectories. The code for InBrainSyn is available at https://github.com/Fjr9516/InBrainSyn.

Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. Biological systems possess the ability not only to self-reconfigure but also to self-classify themselves to determine a general shape and function. Previous work into modular robotics systems has only enabled self-recognition and self-reconfiguration into a specific target shape, missing the inherent robustness present in nature to self-classify. In this paper we therefore take advantage of recent advances in deep learning and neural cellular automata, and present a simple modular 2D robotic system that can infer its own class of shape through the local communication of its components. Furthermore, we show that our system can be successfully transferred to hardware which thus opens op-portunities for future self-classifying machines. Code available at https://github.com/kattwalker/projectcube. Video available at https://youtu.be/0TCOkE4keyc.

Manufacturers of lithium-ion batteries inform capacity degradation for regular, symmetrical charge/discharge cycles, which is clearly problematic in real life applications where charge/discharge cycles are hardly regular. In this context, this paper presents a methodology that can model the degradation of lithium-ion batteries when these are charged and discharged erratically. The proposed methodology can model degradation of a lithium-ion battery type subject to erratic charge/discharge cycles where degradation data under symmetrical charge/discharge cycles (namely, under a standard protocol) has been provided by the manufacturer. To do so we use the concepts of (i) SOC swing, (ii) average swing range and (iii) Coulombic efficiency to model the degradation process in a simple manner through interpolation techniques. We use both deterministic and Monte Carlo simulations to obtain capacity degradation as a function of the number of cycles.

Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high-level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

This paper introduces a new methodology to compute fabric tensors from computational fluid dynamics simulations performed through the lattice-Boltzmann method. Trabecular bone is modeled as a pipeline where a synthetic viscous fluid can flow from a single source located at the center of a spherical region of interest toward its boundaries. Two fabric tensors are computed from local velocities at the steady state estimated from the simulations, a tortuosity and a normalized tortuosity tensor. The main advantage of the proposed fabric tensors is that, unlike previous approaches, they intentionally disregard the trabecular termini in the computations, which do not play an important role in the estimation of trabecular bone quality. Thus, the proposed fabric tensors are less prone than previously proposed ones to unnecessary reductions of anisotropy related to the the presence of trabecular termini. The results of experiments conducted on synthetic and micro-computed tomography data in 2D and 3D show the artificial fluid flowing inside the trabecular bone has negligible velocities at trabecular termini, reducing in that way their influence in the estimation of the proposed fabric tensors.

This paper presents a hybrid strategy for controlling a chain type modular robot to achieve a better interaction with the environment based on the combination of Central Pattern Generators (CPG) and Hormone inspired control. Locomotion movements of robot chains are generated by the local interaction of CPG running inside each module. Hormone messages are used to propagate sensory feedback information to the CPG thus providing a way to define general motor primitives regardless of the specific role of a module and the configuration of the robotic structure. An example set of motor primitives is defined using the hybrid approach and tested in a simulated environment. Our results showed that the integration of the motor primitives using the hybrid strategy allows the robot to travel through an obstacle-filled uneven terrain.

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homosgeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art.

This paper presents a new method for color edge detection based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information via tensors in order to propagate them into a neighborhood through a voting process specifically designed for color edge detection by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorithm is more robust and has a similar performance in precision when compared with the state-of-the-art.

This paper defines a new methodology for evaluating edge detectors through measurements on edginess maps instead of on binary edge maps as previous methodologies do. These measurements avoid possible bias introduced by the application-dependent process of generating binary edge maps from edginess maps. The features of completeness, discriminability, precision and robustness, which a general-purpose edge detector must comply with, are introduced. The R, DS, P and FAR-measurements in addition to PSNR applied to the edginess maps are defined to assess the performance of edge detection. The R, DS, P and FAR-measurements can be seen as generalizations of previously proposed measurements on binary edge maps. Well-known and state-of-the-art edge detectors have been compared by means of the new proposed metrics. Results show that it is difficult for an edge detector to comply with all the proposed features.