Abstract: Light emitting structures and methods of fabrication are described. In an embodiment, LED coupons are transferred to a carrier substrate and then patterned to LED mesa structures. Patterning may be performed on heterogeneous groups of LED coupons with a common mask set. The LED mesa structure are then transferred in bulk to a display substrate. In an embodiment, a light emitting structure includes an arrangement of LEDs with different thickness, and corresponding bottom contacts with different thicknesses bonded to a display substrate.

Abstract: The present invention relates to a method for preparing 2-[2-(2-chlorothiazol-5-yl)-2- hydroxy-ethyl]sulfanyl-6-hydroxy-3-methyl-5-phenyl-pyrimidin-4-one or a tautomer thereof or enantiomerically enriched forms thereof, to 2-[2-(2-chlorothiazol-5-yl)-2-hydroxy-ethyl]sulfanyl-6-hydroxy-3-methyl-5-phenyl-pyrimidin-4-one or a tautomer thereof or enantiomerically enriched forms thereof, and to the use thereof as intermediate in the preparation of 2,3-dihydrothiazolo[3,2-a]pyrimidinium compounds, specifically of 3-(2-chlorothiazol-5-yl)-8-methyl-7-oxo-6-phenyl-2,3-dihydrothiazolo [3,2-a]pyrimidin-4-ium-5-olate and enantiomerically enriched forms thereof.

Abstract: The present invention relates to a method for preparing 2-[2-(2-chlorothiazol-5-yl)-2-oxo-ethyl]sulfanyl-6-hydroxy-3-methyl-5-phenyl-pyrimidin-4-one or a tautomer thereof, to 2-[2-(2-chlorothiazol-5-yl)-2-oxo-ethyl]sulfanyl-6-hydroxy-3-methyl-5-phenyl-pyrimidin-4-one or a tautomer thereof and to its use as intermediate in the preparation of 2,3-dihydrotheiazolo [3,2-a]pyrimidinium compounds, and specifically of 3-(2-chlorothiazol-5-yl)-8-methyl-7-oxo-6-phenyl-2,3-dihydrothiazolo [3,2-a]pyrimidin-4-ium-5-olate and enantiomerically enriched forms thereof.

Abstract: A split enclosure apparatus for fan-less cooling may be provided. The apparatus may comprise a device and a housing. The device may comprise a plurality of components. The housing may enclose the device and may comprise a first external surface, a second external surface, and a joint between the first external surface and the second external surface. The first external surface may be dedicated to cooling a first one of the plurality of components. The second external surface may be dedicated to cooling a second one of the plurality of components. The joint between the first external surface and the second external surface may be electrically conductive and thermally resistive.

Abstract: A method and system perform single phase and multi-phase contour refinement of lesions. The method includes receiving a three dimensional input mask; receiving input slices from the medical images including a lesion; cropping the input slices with the input mask; performing lesion contour refinement for the cropped input slices and the input mask to obtain a predicted mask; and storing the predicted mask that includes 3D lesion contour refinement. A multiphase method includes deforming the 3D input mask from the reference phase to a target phase or warping the input slices from the target phase to the reference phase before contour refinement. The warped images generate an output mask in the reference phase coordinate system that is then deformed to the target phase coordinate system for display.

Abstract: Methods and systems are provided for detecting focal lesions in multi-phase or multi-sequence medical imaging studies of medical images. One system includes memory for storing medical images and an electronic processor. The electronic processor is configured to: detect native candidate lesions for first phase computer tomography (CT) scans, detect candidate lesions for second phase CT scans, and project the candidate lesions detected for the first phase CT scans and/or the second phase CT scans on a same common domain. Once the candidate lesions are registered together, the electronic processor performs a matching algorithm for each of the native candidate lesions of the first phase CT scans with the registered candidate lesions from the second phase CT scans to determine presence and contours of valid lesions for the medical images, and to discard candidate lesions that are not acceptable.

Abstract: A method and system perform single phase and multi-phase contour refinement of lesions. The method includes receiving a three dimensional input mask; receiving input slices from the medical images including a lesion; cropping the input slices with the input mask; performing lesion contour refinement for the cropped input slices and the input mask to obtain a predicted mask; and storing the predicted mask that includes 3D lesion contour refinement. A multiphase method includes deforming the 3D input mask from the reference phase to a target phase or warping the input slices from the target phase to the reference phase before contour refinement. The warped images generate an output mask in the reference phase coordinate system that is then deformed to the target phase coordinate system for display.

Abstract: Mechanisms are provided for detecting lesions in diffusion weighted imaging (DWI) images. The mechanisms receive a first set of DWI images corresponding to a anatomical structure, from medical imaging computer system(s). The first set of DWI images comprises a plurality of DWI images having at least two different b-values. The mechanisms generate a second set of DWI images from the first set of DWI images based on at least one predetermined criterion. The second set of DWI images comprises different DWI images having different b-values. The mechanisms extract feature data from the second set of DWI images, input the feature data into at least one computer neural network, and generate an output from the neural network(s) comprising at least one of a lesion classification or a lesion mask based on results of processing, by the neural network(s), of the feature data extracted from the second set of DWI images.

Abstract: Computer technology for scheduling viewing of sets of medical images for evaluation of medical images (for example X-ray images) by a medical professional (for example, a radiologist). The scheduling is based on, at least in part, scheduling rules obtained from computerized analysis of historical viewing patterns of the medical professional and/or other similarly situated medical professional viewers. The scheduling may include various types of scheduling, such as time of day scheduling, date scheduling (that is, day of the week / month / year scheduling, order of images to be viewed within a set of patient images, order of viewing among and between the various patient sets of images and amount of time to be spent on each image, each set of images.

Abstract: A method and system perform single phase and multi-phase contour refinement of lesions. The method includes receiving a three dimensional input mask; receiving input slices from the medical images including a lesion; cropping the input slices with the input mask; performing lesion contour refinement for the cropped input slices and the input mask to obtain a predicted mask; and storing the predicted mask that includes 3D lesion contour refinement. A multiphase method includes deforming the 3D input mask from the reference phase to a target phase or warping the input slices from the target phase to the reference phase before contour refinement. The warped images generate an output mask in the reference phase coordinate system that is then deformed to the target phase coordinate system for display.

Abstract: A mechanism is provided for seed relabeling for seed-based slice-wise lesion segmentation. The mechanism receives a lesion mask for a three-dimensional medical image volume. The lesion mask corresponds to detected lesions in the medical image volume and wherein each detected lesion has a lesion contour. The mechanism generates a distance map for a given two-dimensional slice in the medical image volume based on the lesion mask. The distance map comprises a distance to a lesion contour for each voxel of the given two-dimensional slice. The mechanism performs local maxima identification to select a set of local maxima from the distance map such that each local maximum has a value greater than its immediate neighbor points. The mechanism performs seed relabeling based on the distance map and the set of local maxima to generate a set of seeds. Each seed represents a center of a distinct component of a lesion contour.

Abstract: Disclosed are techniques for automated analysis and assessments of contrast medium absorption phases in contrast medium based medical images. A target image set includes a plurality of medical images acquired to image a plurality of contrast medium absorption phases. For the images of the target image set, a set of contrast medium absorption phase probabilities are determined corresponding to likelihoods that a given image corresponds to a given contrast medium absorption phase. The determined sets of contrast medium absorption phases are compared against a reference set of contrast medium absorption phases to determine differences to determine a set of matching scores indicative of how closely the contrast medium absorption phases of the target image set align with the plurality of contrast medium absorption phases as compared to the reference set of contrast medium absorption phases.

Abstract: Mechanisms for identifying inconsistencies between volumes of medical images are provided. A plurality of volumes of medical images are received, each having a plurality of medical images of an anatomical structure. A plurality of first representation data structures are generated, each corresponding to a volume and having dimensional measurements of the anatomical structure at various locations. A reference data structure is generated, for the anatomical structure based on the first representation data structures, having second dimensional measurements derived from the first dimensional measurements. A discrepancy is detected between a second representation data structure and the reference data structure based on a comparison and a notification of the discrepancy where the notification identifies a type of the discrepancy.

Abstract: A mechanism is provided to implement a trained machine learning computer model for determining z-wise lesion connectivity. The mechanism identifies, for a given slice in a three-dimensional medical image, a first lesion in the given slice and a second lesion in an adjacent slice in the three-dimensional medical image. The mechanism determines a first intersect value between the first lesion and the second lesion with respect to the first lesion and determines a second intersect value between the first lesion and the second lesion with respect to the second lesion. The mechanism determines whether the first lesion and the second lesion belong to the same three-dimensional lesion based on the first and second intersect values.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Abstract: A lesion detection ensemble machine learning model architecture comprising a plurality of trained machine learning (ML) computer models is provided. A first decoder of a lesion detection ML model processes a medical image input to generate a first lesion mapping prediction. A second decoder of the lesion detection ML model processes the medical image input to generate a second lesion mapping prediction. Combinational logic combines the first and second lesion mapping predictions to generate a combined prediction. Final lesion mapping output logic generates a final lesion prediction based on the combined lesion mapping prediction. The final lesion mapping output logic outputs the final lesion prediction for further downstream computing operations. The first decoder is trained with a first loss function that is configured to counterbalance a training of the second decoder that is trained using a second loss function different from the first loss function.

Abstract: Methods and systems of estimating b-values. One system including an electronic processor configured to receive a set of medical images associated with a patient, where the set of medical images are diffusion-weighted images. The electronic processor is also configured to extract a set of patches from each medical image included in the set of medical images. The electronic processor is also configured to determine, via an estimation model trained using machine learning, a set of estimated b-values, where each estimated b-value is associated with a patch included in the set of patches. The electronic processor is also configured to determine a b-value for each of the medical images included in the set of medical images, where the b-value is based on the set of estimated b-values.

Abstract: An imaging system, such as a DBT system, capable of providing an operator of the system with information concerning the location, magnitude and direction of motion detected by the system during performance of the scan to enhance image processing. The imaging system provides the motion information to the operator directly in conjunction with the images processed by the imaging system thereby providing the operator with sufficient information for decisions regarding the need for additional images for completing the scan with the imaging system before the patient is discharged, or even before the breast is decompressed.

Abstract: In an approach for automatically detecting whether an output of a detection and segmentation algorithm is of an acceptable quality, a processor receives an image. A processor applies a detection stage of a detection and segmentation algorithm to the image. A processor computes a set of features from a detection score map output by the detection stage of the detection and segmentation algorithm by analyzing the detection score map at more than one different operating points. A processor inputs the set of features into a classifier that predicts whether a final output of the detection and segmentation algorithm will be of an acceptable quality, wherein the acceptable quality is defined based on whether a detection precision threshold has been reached. A processor receives an output of the classifier.