Abstract: An apparatus in accordance with embodiments of the present disclosure may obtain an image depicting one or more hands of a person in a medical environment; and detect, using a first machine learning (ML) model, a plurality of 2D landmarks associated with a hand of the person depicted in the image. The apparatus may further determine, using a second ML model, 3D features of the hand of the person based on the plurality of 2D landmarks. The apparatus may determine a gesture indicated by the hand of the person based on the 3D features of the hand predicted using the second ML model. Alternatively, in determining the 3D features of the hand, the system may stack the plurality of 2D landmarks across a sequence of image frames in a video, and use a third ML model to determine the 3D features of the hand based on the stacked 2D landmarks.

Abstract: The disclosure provides a hybrid membrane camouflaged nanomedicine loaded with oxidative phosphorylation inhibitor and preparation method thereof. The nanomedicine comprises an inner core and an outer shell coated on the periphery of the inner core. The inner core is a ROS-responsive drug-loaded nanoparticle, and the drug loaded by the ROS-responsive nanocarrier is an oxidative phosphorylation inhibitor. The outer shell is a hybrid membrane of mitochondrial membrane and cancer cell membrane. The nanomedicines can cross the BBB and reach tumor sites by the homologous targeting of cancer cell membrane, and then they can homologously target and enter mitochondria by the mitochondrial membrane. Subsequently, under the high-level ROS environment of the mitochondria, the ROS responsive drug-loaded nanoparticle releases the oxidative phosphorylation inhibitor due to the swell and degradation of the inner core, so that the safe and efficient targeted GBM therapy is achieved.

Abstract: Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.

Abstract: An apparatus for annotating a medical image may be configured to obtain, automatically, an outline of a region of interest (ROI) in the medical image and determine, based on one or more inner control points and one or more outer control points. The one or more inner control points may be located within the ROI and the one or more outer control points may be located outside of the ROI. The outline may be subsequently adjusted based on a user input and the adjusted outline may be used to generate a segmentation of the ROI.

Abstract: Described herein are machine learning (ML) based on systems, methods, and instrumentalities associated with image search and/or retrieval. An apparatus as described herein may obtain a query image and a textual description associated with the query image, and generate, using an artificial neural network (ANN), a feature representation that may represent the image and the textual description as an associated pair. Based on the feature representation, the apparatus may identify one or more images from an image repository and provide an indication regarding the one or more identified images, for example, as a ranked list.

Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

Abstract: An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.

Abstract: A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.

Abstract: Disclosed herein are systems, methods and instrumentalities associated with multi-view 3D human model estimation using machine learning (ML) based techniques. These techniques may use synthetically generated data to train an ML model that may be used to progressively regress a 3D human body model based on multi-view 2D images. The training data may be synthetically generated based on statistical distributions of human poses and human body shapes, as well as a statistical distribution of camera viewpoints. The progressive regression may be performed based on consensus features shared by the multi-view images and diversity features derived from at least one of the multi-view images. Consistency between the multi-view images may also be maintained during the regression process.

Abstract: The present disclosure provides a drug carrier, a brain-targeting nanodrug based on CRISPR gene editing technology and a preparation method and use thereof. The nanodrug contains nanoparticles prepared by coupling Cas9/sgRNA and drug carriers. The drug carrier includes a polymer mPEG-P (GPMA, FPMA) and a polymer Ang-PEG-PGPMA, wherein a structural formula of the mPEG-P (GPMA, FPMA) is: a structural formula of the polymer Ang-PEG-PGPMA is: where n is 35-45, x1 is 15-20, y is 2-4, m is 75-85, and x2=x1. The guanidino group of the drug carrier can be combined with the ribonucleoprotein complex by electrostatic action, salt bridge formation, or hydrogen bonding action. Also provided are methods of suppressing and treating tumors at a gene level using the drug carrier to transport the therapeutic drug to the lesion site.

Abstract: The physical characteristics of one or more anatomical structures of a person may change in accordance with conditions surrounding the determination of such physical characteristics. Machine learning based techniques may be used to determine a template representation of the one or more anatomical structures that may indicate the physical characteristics of the one or more anatomical structures free of the impact imposed by changing conditions. The template representation may then be used to predict the physical characteristics of the one or more anatomical structures under a new set of conditions, without subjecting the person to additional medical scans.

Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Abstract: Human model recovery may be realized utilizing pre-trained artificially neural networks. A first neural network may be trained to determine body keypoints of a person based on image(s) of a person. A second neural network may be trained to predict pose parameters associated with the person based on the body keypoints. A third neural network may be trained to predict shape parameters associated with the person based on depth image(s) of the person. A 3D human model may then be generated based on the pose and shape parameters respectively predicted by the second and third neural networks. The training of the second neural network may be conducted using synthetically generated body keypoints and the training of the third neural network may be conducted using normal maps. The pose and shape parameters predicted by the second and third neural networks may be further optimized through an iterative optimization process.

Abstract: Disclosed herein are systems, methods and instrumentalities associated with multi-person joint location and pose estimation based on an image that depicts multiple people in a scene, where at least some of the joint locations of a person may be blocked or obstructed by other people or objects in the scene. The estimation may be performed by detecting and grouping joint locations in the image using a bottom-up approach, and refining each group of detected joint locations by recovering obstructed joint location(s) that may be missing from the group. The detection, grouping, and/or refinement may be accomplished based on one or more machine learning (ML) models that may be implemented using artificial neural networks such as convolutional neural networks.

Abstract: Automatic hand gesture determination may be a challenging task considering the complex anatomy and high dimensionality of the human hand. Disclosed herein are systems, methods, and instrumentalities associated with recognizing a hand gesture in spite of the challenges. An apparatus in accordance with embodiments of the present disclosure may use machine learning based techniques to identify the area of an image that may contain a hand and to determine an orientation of the hand relative to a pre-defined direction. The apparatus may then adjust the area of the image containing the hand to align the orientation of the hand with the pre-defined direction and/or to scale the image area to a pre-defined size. Based on the adjusted image area, the apparatus may detect a plurality of hand landmarks and predict a gesture indicated by the hand based on the plurality of detected landmarks.

Abstract: The present disclosure provides a gene editing nanocapsule and a preparation method and use thereof. The gene editing nanocapsule has a core-shell structure, wherein the inner core includes a Cas/sgRNA ribonucleoprotein complex, and the outer shell includes a polymer, the Cas/sgRNA ribonucleoprotein complex has a gene editing function, and the polymer acts as a carrier for the Cas/sgRNA ribonucleoprotein complex and protects it, because the polymer contains tumor microenvironment sensitive molecules, the nanocapsules can be efficiently released in tumor cells. Further, the surface of the outer shell can be modified with a targeting agent, so that the nanocapsule can specifically target tumor cells, which improves the endocytosis efficiency of the nanocapsule. The gene editing nanocapsule has good biocompatibility and biosafety, and is expected to become a safe and efficient gene therapy drug for tumors.

Abstract: The present disclosure discloses preparation and use of sugar-targeting nanoparticles for modifying siRNA. A sugar-targeting nanoparticle, including targeting nanocarriers, wherein the targeting nanocarriers are formed by linking in sequence a targeting molecule, a first linking compound, a first hydrophilic biomaterial, a second linking compound and a cationic compound through chemical bonds; the first linking compound and the second linking compound both have a carboxyl group; the first linking compound has a maleimido group at the same time, and the targeting molecule is a cycloaldohexose.

Abstract: A person's privacy is protected by the law in many settings and disclosed herein are systems, methods, and instrumentalities associated with anonymizing an image of a person while still preserving the visual saliency and/or utility of the image for one or more downstream tasks. These objectives may be accomplished using various machine-learning (ML) techniques such as ML models trained for extracting identifying and residual features from the input image as well as ML models trained for transforming the identifying features into identity-concealing features and for preserving the utility features of the image. An output image may be generated based on the various ML models, wherein the identity of the person may be substantially disguised in the output image while the background and utility attributes of the original image may be substantially maintained in the output image.

Abstract: The shape and/or location of an organ may change in accordance with changes in the body shape and/or pose of a patient. Described herein are systems, methods, and instrumentalities for automatically determining, using an artificial neural network (ANN), the shape and/or location of the organ based on human models that reflect the body shape and/or pose the patient. The ANN may be trained to learn the spatial relationship between the organ and the body shape or pose of the patient. Then, at an inference time, the ANN may be used to determine the relationship based on a first patient model and a first representation (e.g., a point cloud) of the organ so that given a second patient model thereafter, the ANN may automatically determine the shape and/or location of the organ corresponding to the body shape or pose of the patient indicated by the second patient model.