Jiri Jan

TL;DR: The concept of matched filtering is improved, and the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods, and outperforms most of them with an accuracy of 95% evaluated on the new database.

TL;DR: This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art Kalman filtering, which promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous input.

TL;DR: It is essential that differently oriented specialists and students involved in image processing have a firm grasp of the necessary concepts and principles, as well as a thorough explanation of the techniques involved, particularly those found in medical image processing.

TL;DR: This work proposes an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data and demonstrates the effectiveness and robustness of the dynamic approach, significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling.

TL;DR: The segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography images obtained from highly pathologically affected cases are addressed by automatic feature extraction provided by a deep Convolutional Neural Network.