Abstract: An Image Activated Cell Sorting (IACS) classification workflow includes: employing a neural network-based feature encoder (or extractor) to extract features of cell images; automatically clustering cells based on extracted cell features; identifying a cluster to pick which cluster(s) to sort based on the cell images; fine-tuning a classification network based on the cluster(s) selected; and once refined, the classification network is used to sort cells for real-time live sorting.

Abstract: A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Abstract: Implementations generally perform pose reconstruction by tracking for video analysis. In some implementations, a method includes obtaining a plurality of videos of at least one subject performing at least one action in an environment. The method further includes tracking the at least one subject across at least two cameras. The method further includes reconstructing a 3-dimensional (3D) model of the at least one subject based on the plurality of videos and the tracking of the at least one subject.

Abstract: Implementations generally relate to surgical scene assessment based on computer vision. In some implementations, a method includes receiving a first image frame of a plurality of image frames associated with a surgical scene. The method further includes detecting one or more objects in the first image frame. The method further includes determining one or more positions corresponding to the one or more objects. The method further includes tracking each position of the one or more objects in other image frames of the plurality of image frames.

Abstract: Implementations generally perform robust multi-view multi-target action recognition using reconstructed 3-dimensional (3D) poses. In some implementations, a method includes obtaining a plurality of videos of a plurality of subjects in an environment, where at least one target subject of the plurality of subjects performs one or more actions in the environment. The method further includes tracking the at least one target subject across at least two cameras. The method further includes reconstructing a 3-dimensional (3D) model of the at least one target subject based on the plurality of videos and the tracking of the at least one target subject. The method further includes recognizing the one or more actions of the at least one target subject based on the reconstructing of the 3D model.

Abstract: A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.

Abstract: Implementations generally recognize clinical activity using multiple cameras. In some implementations, a method includes obtaining a plurality of videos of a plurality of objects in an environment. The method further includes determining one or more key points for each object of the plurality of objects. The method further includes recognizing activity information based on the one or more key points. The method further includes computing workflow information based on the activity information.

Abstract: Implementations generally perform pose reconstruction by tracking for video analysis. In some implementations, a method includes obtaining a plurality of videos of at least one subject performing at least one action in an environment. The method further includes tracking the at least one subject across at least two cameras. The method further includes reconstructing a 3-dimensional (3D) model of the at least one subject based on the plurality of videos and the tracking of the at least one subject.

Abstract: An electronic device for smoke estimation is provided. The electronic device receives a first image of a plurality of images of a physical space. The electronic device detects smoke in the physical space based on an application of a trained neural network model on the received first image. The electronic device generates a heatmap of the physical space based on the detected smoke in the physical space, and further based on an output of the trained neural network model corresponding to the detection of the smoke. The electronic device estimates a level of the smoke in the physical space based on a normalization of the generated heatmap.

Abstract: Implementations generally relate to determining the position of a tool tip of a surgical instrument and the orientation of the surgical instrument. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting a surgical tool in the at least one image frame. The method further includes determining a position of a tip of the surgical tool. The method further includes determining an orientation of the surgical tool.

Abstract: An image-based classification workflow uses unsupervised clustering to help a user identify subpopulations of interest for sorting. Labeled cell images are used to fine-tune the supervised classification network for a specific experiment. The workflow allows the user to select the populations to sort in the same manner for a variety of applications. The supervised classification network is very fast, allowing it to make real-time sort decisions as the cell travels through a device. The workflow is more automated and has fewer user steps, which improves the ease of use. The workflow uses machine learning to avoid human error and bias from manual gating. The workflow does not require the user to be an expert in image processing, thus increasing the ease of use.

Abstract: A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Abstract: A method for super-resolution of block-compressed texture is provided. The method includes receiving a first texture block of a first block size. Based on application of a first block compression (BC) scheme on the received first texture block, coded-texture values are generated in a compressed domain. Further, a first machine learning model is applied on the generated coded-texture values to generate super-resolution coded-texture values in the compressed domain. The generated super-resolution coded-texture values are processed to generate a second texture block of a second block size. The second block size is greater than the first block size.

Abstract: Implementations generally relate to surgical scene assessment based on computer vision. In some implementations, a method includes receiving a first image frame of a plurality of image frames associated with a surgical scene. The method further includes detecting one or more objects in the first image frame. The method further includes determining one or more positions corresponding to the one or more objects. The method further includes tracking each position of the one or more objects in other image frames of the plurality of image frames.

Abstract: A method for automatic organ segmentation in CT images comprises in a first step, rough region segmentation; in a second step, coarse organ segmentation; and in a third step, refinement of organ segmentation. The organ may be a liver. Rough region segmentation may comprise applying standard anatomical knowledge to the CT images. Coarse segmentation may comprise identifying organ voxels using a probabilistic model. Refinement of organ segmentation may comprise refinement based on intensity, followed by refinement based on shape. Apparatuses configured to carry out the method are also disclosed.

Abstract: Implementations generally relate to determining tool handedness for surgical videos. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting one or more objects in the at least one image frame. The method further includes classifying the one or more objects into one or more tool classifications, where the one or more objects are tools. The method further includes determining, for each tool, if the tool is assistive or non-assistive. The method further includes determining, for each tool, a handedness of the tool.

Abstract: Implementations generally relate to determining the position of a tool tip of a surgical instrument and the orientation of the surgical instrument. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting a surgical tool in the at least one image frame. The method further includes determining a position of a tip of the surgical tool. The method further includes determining an orientation of the surgical tool.

Abstract: Blind legacy video artifact reduction reduces ghost artifacts and Y/C artifacts. Ghost artifacts are reduced using edge correlation and an IIR filter. Y/C artifacts are reduced using frequency domain filtering. The result is much cleaner video content.

Abstract: Implementations generally relate to detecting dominant tools in surgical videos. In some implementations, a method includes receiving at least one image frame. The method further includes detecting one or more objects in the at least one image frame. The method further includes classifying the one or more objects into one or more tool classifications, where the one or more objects are tools. The method further includes determining a handedness of the one or more tools. The method further includes determining a dominant tool from the one or more tools based at least in part on the one or more classifications of the one or more tools and based at least in part on the handedness of the one or more tools.

Abstract: A method for 2D feature tracking by cascaded machine learning and visual tracking comprises: applying a machine learning technique (MLT) that accepts as a first MLT input first and second 2D images, the MLT operating on the images to provide initial estimates of a start point for a feature in the first image and a displacement of the feature in the second image relative to the first image; applying a visual tracking technique (VT) that accepts as a first VT input the initial estimates of the start point and the displacement, and that accepts as a second VT input the two 2D images, processing the first and second inputs to provide refined estimates of the start point and the displacement; and displaying the refined estimates in an output image.