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Anatomical, Genomics, along with Reactions to be able to Tensions within Cyanobacteria: Biotechnological Ramifications.
We verify these results empirically in an extensive validation on six medical segmentation tasks and can confirm that metric-sensitive losses are superior to cross-entropy based loss functions in case of evaluation with Dice Score or Jaccard Index. This further holds in a multi-class setting, and across different object sizes and foreground/background ratios. These results encourage a wider adoption of metric-sensitive loss functions for medical segmentation tasks where the performance measure of interest is the Dice score or Jaccard index.Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. check details eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.Photon counting computed tomography (PCCT) has the ability to identify individual photons, resulting in quantitative material identification. Meanwhile, several technical challenges still exist in current PCCT imaging systems, including increased noise and suboptimal bin selection. These nonideal effects can substantially degrade the reconstruction performance and material estimation accuracy. To address these issues, in this work, we present a novel system for high-performance spectral PCCT imaging, which is a combination of multiple dynamic modulations, interpolation-based measurements processing strategy and advanced reconstruction method. For simplicity, this new PCCT imaging system is referred to as "MDM-PCCT". Specifically, the multiple dynamic modulations consist of dynamic kVp modulation, dynamic spectrum modulation and dynamic energy threshold modulation. In the dynamic kVp modulation, three kVp values, i.e., 80, 110 and 140, are included, and the tube voltage waveform follows a sinusoidal curve whical decomposition accuracy.During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( μ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.
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