Abstract: To plan tumor treating fields (TTFields) therapy, a model of a patient's head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

Abstract: Provided is a disease diagnosis support method employing endoscopic images of a digestive organ using a neural network, and the like. The disease diagnosis support method employing endoscopic images of a digestive organ using a neural network trains the neural network by using first endoscopic images of the digestive organ, and corresponding to the first endoscopic images, at least one of definitive diagnosis result of being positive or negative for the disease of the digestive organ, a past disease, a severity level, and information corresponding to an imaged region. The trained neural network outputs, based on second endoscopic images of the digestive organ, at least one of a probability of being positive and/or negative for the disease of the digestive organ, a probability of a past disease, a severity level of the disease, and the information corresponding to the imaged region.

Abstract: Disclosed herein are a system and method that may help place or position a component, such as an acetabular cup or a femoral component, during surgery. An example system may iteratively register a plurality of two-dimensional projections from a three-dimensional model of a portion of a patient, the three-dimensional model being generated from a data set of imaging information obtained at a neutral position. An example system may further score each two-dimensional projection against an intra-operative image by calculating a spatial difference between corresponding points. A two-dimensional projection having a minimum score reflecting the smallest distance between the corresponding points may be identified.

Abstract: A culture information processing device includes: a feature value computing unit that computes growth feature values indicating features of growth characteristics of cells from data acquired in a particular first subculturing process selected from among a plurality of subculturing processes included in a culture period of the cells; a condition setting unit that sets culturing conditions of a second subculturing process one process after the first subculturing process; and an information computing unit that computes, on the basis of the growth feature values computed by the feature value computing unit and the culturing conditions set by the condition setting unit, characteristics-related information related to growth characteristics in the second subculturing process.

Abstract: A system and method include acquisition of a plurality of event data associated with an object, each of the plurality of event data associated with a position and a time, assigning of each event data to one of a plurality of time-based frames based on a time associated with the event data, each of the plurality of time-based frames associated with a respective time period, assigning of each event data to one of a plurality of motion-based frames based on a time associated with the event data, each of the plurality of motion-based frames associated with a respective time period associated with a respective motion state, determination of a confidence score based on the plurality of time-based frames of event data and on the plurality of motion-based frames of event data, and presentation of the confidence score and a control selectable to initiate motion-correction of the event data.

Abstract: When generating a tomographic image using a measuring X-ray CT apparatus that is configured to emit X-rays while rotating a specimen that is arranged on a rotary table and reconstruct a projection image thereof to generate a tomographic image of the specimen, an amount of geometric error that is included in the projection image is obtained in advance and stored; the projection image is corrected using the stored amount of geometric error; and a tomographic image is reconstructed using the corrected projection image.

Abstract: A medical image processing system includes a memory and a processor coupled to each other. The processor accesses and executes instructions which memory stores to perform the following: obtaining a plurality of brain MR images corresponding to a subject, wherein the brain MR images corresponds to a subject brain space; accessing a DBS targets atlas corresponding to a specific stimulation area; transforming the DBS targets atlas from a MNI brain space to the subject brain space based on a DARTEL algorithm; marking at least one coordinate having a largest Voxel value in the brain MR images based on the transformed DBS targets atlas; and storing the brain MR images being targeted with the at least one coordinate into a predetermined format corresponding to a guiding device so that the guiding device displays the brain MR images being targeted with the at least one coordinate for guidance in DBS procedure.

Abstract: A method for obtaining a probability in a 3D probability map, includes: obtaining at least one value of at least one parameter for each stop of a 3D moving window, wherein a first, second, third and fourth of the stops are partially overlapped, the first and second stops are shifted from each other by a distance equal to a first dimension of a computation voxel, the first and third stops are shifted from each other by a distance equal to a second dimension of the computation voxel, and the first and fourth stops are shifted from each other by a distance equal to a third dimension of the computation voxel; matching the at least one value to a classifier to obtain a first probability for each stop of the 3D moving window; and calculating a second probability for the computation voxel based on information associated with the first probabilities for the first through fourth stops.

Abstract: A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastritis, in a subject are disclosed. The diagnostic system includes a processor that obtains an image of a stomach of the subject collected by an endoscope. The processor can compare the subject image with reference normal and abnormal stomach images. If the subject image is abnormal, the processor can generate a subject abnormality image indicative of the differences between the subject image and the normal image. By comparing the generated subject abnormality image with reference abnormality images representative of different severity levels of gastritis, the processor can diagnose the subject as having a particular severity level of gastritis.

Abstract: An automatic classification method of whole slide images (WSIs) for cervical tissue pathology based on confidence coefficient selection. The automatic classification method includes steps: S1: dividing the WSIs for the cervical tissue pathology into small pieces having set size, gathering the small pieces of each WSI into a packet, and removing blank pieces in the packets; S2: building a deep CNN model; S3: training the deep CNN for designated rounds; S4: performing sequential arrangement and connection to obtain feature vectors of WSIs by using the trained deep CNN as the feature extractor; S5: training a support vector machine classifier; and S6: processing the WSIs for the cervical tissue pathology, to be classified, through step S1 and step S4 to obtain the feature vectors of the images, and inputting the feature vectors into the trained support vector machine classifier to realize classification.

Abstract: An ultrasound imaging system and method includes acquiring ultrasound image data while moving an ultrasound probe, automatically identifying a plurality of segments of interest in the ultrasound image data, automatically applying temporal scaling to at least one of the plurality of segments of interest, and displaying the ultrasound image data as a panoramic view comprising a plurality of videos, where each of the plurality of videos is based on a different one of the plurality of the segments of interest, and where, based on the temporal scaling, each of the plurality of videos in the panoramic view takes the same amount of time to play.

Abstract: A first machine learning model is trained to classify dental anatomy and/or pathologies represented in an input dental image or to generate a label (pixel mask) for dental anatomy and/or pathologies represented in the input dental image. A final layer, such as one of two fully connected layers, may be removed from the first machine learning model to obtain a modified machine learning model. Hidden features output from the modified machine learning model may be input to a LSTM model that outputs a text sequence. The LSTM model may be trained with images labeled with text sequences to output a text sequence for a given input dental image.

Abstract: Devices, systems, methods, and computer-readable media for registering an electromagnetic registration of a luminal network to a 3D model of the luminal network include accessing a 3D model of a luminal network based on computed tomographic (CT) images of the luminal network, selecting a plurality of reference points within the 3D model of the luminal network, obtaining a plurality of survey points within the luminal network, dividing the 3D model of the luminal network and the luminal network into a plurality of regions, assigning a plurality of weights to the plurality of regions, determining an alignment of the plurality of reference points with the plurality of survey points based on the plurality of weights, and generating a registration based on the alignment, the registration enabling conversion of the plurality of survey points within the luminal network to points within the 3D model of the luminal network.

Abstract: A method for rapid antibiotic susceptibility testing by tracking sub-micron scale motion of single bacterial cells including obtaining a biological sample from a subject including live bacteria. Different doses of antibiotic are added to a multi-well glass slide and adding portions of the biological sample to the wells. Bacterial cells are tethered onto the surface. The tethered bacterial cells are imaged and tracked. Bacterial sub-micron motion of tethered cells is measured at the different doses. A processor performs statistical analysis on a population of cells for each antibiotic dose to generate an antibiotic dose curve proportional to the motion changes, where the antibiotic dose curve plots data including a decrease in movement over time indicating a proportional effectiveness of an antibiotic applied to a well.

Abstract: A method for updating a patient registration during a medical procedure is disclosed. The medical procedure uses an optical navigation system for optically tracking a patient reference object in a real-world space. The method includes: acquiring a first set of image data depicting a region of interest on the patient while a first patient registration is intact; detecting that the first patient registration has been lost; obtaining a second set of image data depicting the region of interest; and applying a transform to data points of the first region to obtain data points for an updated patient registration, the transform obtained based on the first and second sets of image data.

Abstract: A system including a processor, and memory having stored thereon instructions that, when executed by the processor, control the processor to receive image data of a sequence of images, and a current image of the sequence of images being after a previous image in the sequence of images, each of the current and previous images including a cell, filter the current image to remove noise, iteratively deconvolve the filtered current image to identify edges of the cell within the current image based on determined edges of the cell within the previous image, and segment the deconvolved current image to determine edges of the cell within the current image.

Abstract: Systems and methods for image processing are provided in the present disclosure. The systems may generate a preliminary image by filtering image data generated by an image acquisition device. The system may generate an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image. The first objective function may include a first term associated with a first difference between the intermediate image and the preliminary image, a second term associated with continuity of the intermediate image and a third term associated with sparsity of the intermediate image. The systems may also generate a target image by performing, based on a second objective function, a second iterative operation on the intermediate image. The second objective function may be associated with a system matrix of the image acquisition device and a second difference between the intermediate image and the target image.

Abstract: A machine learning model is trained to predict pixel spacing, distance, and volumetric measurements. Training images are obtained by inpainting around an original image and scaling the inpainted image to obtain the training image having a different pixel spacing than the original image. The machine learning model may include an encoder, a transformer, a first TC layer, and a second TC layer. During training, loss may be obtained from a comparison of the output to the first TC layer to a coarse pixel spacing matrix and a comparison of the output of the second TC layer to a fine pixel spacing matrix. During utilization, the pixel spacing of an image may be obtained using the machine learning model and used to correct the image or measurements obtained from the image.

Abstract: A system and method include acquisition of a plurality of sets of images which meet acceptance criteria of an imaging task, each set of images acquired using a respective instance of a type of imaging component, acquisition of a test image using a test instance of the type of imaging component, presentation of a plurality of groups of images, each of the groups of images including the test image and a respective one or more images of the plurality of sets of images, reception, for each group of images, of an indication from an observer of a ranking of the test image of the group with respect to the respective one or more images of the group, and determination of a quality of the test instance of the type of imaging component based on the indications.

Abstract: The present disclosure provides an operating method of an automatic analysis system on magnetic resonance imaging (MRI), which includes steps as follows. Images are received from of the subject's brain from the MRI machine. Contrast-enhanced T1-weighted images and T2-weighted images are obtained from the images, and the pre-processing is performed on the images. The ratio of T2-weighted images to contrast-enhanced T1-weighted images is calculated to generate contrast-enhanced images. The unsupervised clustering is performed on the region of interest in the contrast-enhanced image to separate a cystic part and a non-cystic part so as to calculate the feature parameters. After radiosurgery is performed on the brain tumor corresponding to the region of interest, the volume change of the tumor is analyzed. The linear regression analysis of the feature parameters and the volume change of the tumor is performed for prognostic evaluation.