Abstract: The present invention relates to methods for predicting the recurrence risk of hepatocellular carcinoma (HCC). Specifically, it proposes a deep learning model capable of integrating information from different phases of CT images and clinical data to predict the risk of HCC recurrence within 1 to 5 years after treatment. Experimental results demonstrate that these models outperform traditional prediction methods based on histological microvascular invasion (MVI) in predicting HCC recurrence risk.

Abstract: An apparatus for enhancing an input phase distribution (I(xi)) is configured to retrieve the input phase distribution (I(xi)) and compute a baseline estimate (ƒ(xi)) as an estimate of a baseline (I2 (xi)) in the input phase distribution (I(xi)). The apparatus is further configured to obtain an output phase distribution (O(xi)) based on the baseline estimate (ƒ(xi)) and the input phase distribution (I(xi)).

Abstract: The invention relates to a data processing device for an imaging apparatus, the device being configured to obtain a length threshold value, to access input image data representing at least one digital input image, wherein the input image data comprise a shading signal representing a brightness decrease towards the edges of the at least one digital input image, and a content signal representing image features of the at least one digital input image, the image features having a length that is smaller than the length threshold value, to compute a baseline image based on the input image data and the length threshold value, wherein the baseline image is representative of an estimate of the shading signal, to generate at least one digital output image representative of an estimate of the content signal by at least one of a) subtracting the baseline image from the input image data and b) dividing the input image data by the baseline image.

Abstract: A novel medical image generation approach synthesizes thin-cut computerized tomography (CT) images from thick-cut CT ones as inputs. First thick-cut CT images are obtained by maximizing the pixel-wise intensity of five or more than five continuous thin-cut ones after image registration. Second, the obtained thick-cut CT images are fed into a generator block which adopts an encoder-decoder architecture, where each thick-cut image is encoded into low-dimensional embedding space before decoding into multiple thin-cut ones. Third, a discriminator focuses on distinguishing original real thin-cut images from synthetic thin-cut images. An adversarial mechanism between the generator and discriminator causes the discriminator's output to provide an effective gradient update of the network parameters for the generator to increasingly improve the generator's ability to synthesize higher-quality thin-cut images and in turn promotes the discriminator's discriminating capability.

Abstract: A three dimensional classification system for recognizing cross-sectional images automatically contains a processor that executes: (1) rescaling of a plurality of cross-sectional images; and feeding the rescaled plurality of cross-sectional images into two branches; (2) feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases; (3) feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, and then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and (4) concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.

Abstract: A data processing apparatus for processing a digital input image is configured to receive the digital input image. The digital input image includes input photon arrival-time data at input image locations. The data processing apparatus is further configured to compute a digital output image based on the digital input image by deconvolution. The digital output image includes output photon arrival-time data at output image locations. The output photon arrival-time data represent an estimate of an unblurred ground-truth of the input photon arrival-time data. The data processing apparatus is further configured to compute the deconvolution by an iterative algorithm using an update function. The update function depends on a point-spread function, a previous estimate of the ground-truth, and the input photon arrival-time data.

Abstract: A marker for marking a predetermined structure within a biological sample includes a marker base having an affinity reagent, and an attachment structure connected to the affinity reagent having two attachment sites. The attachment structure includes a cleavage site arranged between the attachment sites. The attachment structure is capable of being cut at the cleavage site by a cleaving agent in order to remove an attachment site from the marker base. The marker further includes at least two reporters. Each reporter includes a linker structure having a complementary attachment site configured to attach to one of the attachment sites, and a combination of at least two different fluorescent dyes. The combination of the at least two different fluorescent dyes is unique for each reporter. The complementary attachment site is unique for each reporter and configured such that each reporter attaches to a different attachment site of the marker base.

Abstract: Disclosed is a computer-implemented three-dimensional image classification system (CIS) for processing and/or analyzing non-contrast computed tomography (CT) medical imaging data. The CIS is a deep neural network containing multiple Convolutional Block Attention Module (CBAM) blocks, which contain convolutional layers for feature extraction followed by CBAMs. The CBAM applies channel attention to highlight more relevant features and spatial attention to focus on more important regions. Max pooling layers operably link adjacent pairs of CBAM blocks. The output of the final CBAM block is passed to two terminal fully connected layers to generate a diagnosis. This classification system can be used to perform efficient diagnosis of hepatocellular carcinoma using solely non-contrast CT images, with diagnostic performance comparable to that of a radiologist using the current LIRADS system.

Abstract: This disclosure relates to the field of molecular biology. Provided are novel genes that encode pesticidal proteins. These pesticidal proteins and the nucleic acid sequences that encode them are useful in preparing pesticidal formulations and in the production of transgenic pest-resistant plants. Methods to create or alter pesticidal proteins are provided for altered or enhanced pesticidal activity.

Abstract: A method for estimating baseline in a signal, the signal being represented by input signal data (I(xi)), includes estimating a baseline contribution (I2(xi)) in the signal to obtain baseline estimation data (f(xi)), wherein the baseline estimation data (f(xi)) are computed as a fit to at least a subset of the input signal data (I(xi)) by minimizing a least-square minimization criterion (M(f(xi))). Deblurred output signal data (O(xi)) are obtained based on the baseline estimation data (f(xi)) and the input signal data (I(xi)). The least-square minimization criterion (M(f(xi))) comprises a penalty term (P(f(xi))).

Abstract: A method for analysing a biological sample with analytes includes determining information about the biological sample and the analytes. The analytes are marked by a plurality of markers. The method further includes generating a probabilistic model of a distribution of the analytes within the biological sample based on the determined information, generating at least one optical readout of the biological sample, and determining presence of at least one analyte in the at least one optical readout based at least partially on the probabilistic model of the distribution of the analytes within the biological sample.

Abstract: A method for estimating a stimulated emission depletion microscopy (STED) resolution includes generating a first frame representing a reference image from a field-of-view, the reference image having a predetermined reference resolution, and generating at least one second frame representing a STED image from the same field-of-view, the STED image having the STED resolution to be estimated. The at least one second frame is blurred by applying a convolution kernel with at least one fit parameter to the second frame. An optimal value of the at least one fit parameter of the convolution kernel is determined for which a difference between the first frame and the blurred at least one second frame is minimized. The STED resolution is estimated based on the optimal value of the at least one fit parameter and the predetermined reference resolution.

Abstract: A method for improving signal-to-noise of image frames is provided. The method includes estimating a representative velocity of an optical flow in an image frame sequence. The method also includes determining an interpolation factor from the representative velocity of the optical flow. The method also includes employing a trained artificial neural network for generating an expanded image frame sequence. The expanded image frame sequence includes a number of interpolating image frames. Each interpolating image frame interpolates between subsequent image frames of the image frame sequence. The number of interpolating image frames corresponds to the interpolation factor. The method also includes computing a time-dependent combination of image frames from the expanded image frame sequence to generate an output image frame sequence.

Abstract: A digital image processing apparatus for computing a baseline estimate of a digital input image is provided. The digital image processing apparatus is configured to obtain a digital intermediate image by downsampling the digital input image by a predetermined downsampling factor, and compute the baseline estimate based on the digital intermediate image.

Abstract: A method for matching a three-dimensional first image of at least one discrete entity with a three-dimensional second image of the at least one discrete entity is provided. The at least one discrete entity includes a biological sample and a plurality of constituent parts of a marker. The method includes: generating a first representation of the marker from the first image; generating a second representation of the marker from the second image; and based upon the representations matching, matching the first image with the second image; or based upon the representations not matching, rejecting the match. Generating the representations includes determining vectors from at least one reference item to at least some of the constituent parts of the marker, determining for the vectors at least one value of a property, and generating the representations of the marker based on a frequency of the at least one value of the property.

Abstract: An apparatus for baseline estimation in input signal data is configured to retrieve input signal data (I(xi)) and to subtract baseline estimation data (ƒ(xi)) from the input signal data (I(xi)) to compute output signal data. The apparatus is further configured to compute the baseline estimation data (ƒ(xi)) from a convolution using a discrete Green's function (G(xi)).

Abstract: Disclosed are systems and methods using artificial intelligence for the detection and characterization of liver cancers.

Abstract: An apparatus for enhancing an input phase distribution (I(xi)) is configured to retrieve the input phase distribution (I(xi)) and compute a baseline estimate (ƒ(xi)) as an estimate of a baseline (I2 (xi)) in the input phase distribution (I(xi)). The apparatus is further configured to obtain an output phase distribution (O(xi)) based on the baseline estimate (ƒ(xi)) and the input phase distribution (I(xi)).

Abstract: A signal processing apparatus for deblurring a digital input signal (I(xi)) comprising one or more processors. The one or more processors are configured to: compute a plurality of local length scales (lk, l(xi), ?) from at least one of a local signal resolution (FRCk) and a local signal-to-noise ratio (SNRk), and to compute each of the local length scales at a different location (Ik(xi)) of the digital input signal, each of the different locations comprising at least one sample point (xi) of the input signal; compute a baseline estimate (ƒn(xi, ln), ƒ(xi, l(xi))) of the input signal based on the local length scales, the baseline estimate representing signal structures of the digital input signal that are larger than the local length scale; and compute a digital output signal (O(xi)) based on one of (a) the baseline estimate and (b) the digital input signal and the baseline estimate.

Abstract: A computer-implemented system (CIS), based on the DenseNet model, for processing and/or analyzing computer tomography (CT) medical imaging input data is described. The CIS contains two or more dense blocks containing one or more modules. Within each dense block, output from preceding modules containing convolutional layers are transmitted to succeeding modules containing convolutional layers, via a gate that is controlled by a predefined or trainable threshold. The CIS also includes transition layers between the dense blocks, operably linked to pairs of consecutive dense blocks in the series configuration. The CIS can be used in a computer-implemented method for enhanced diagnoses of hepatocellular carcinoma, based analysis of one or more CT medical images.