Abstract: A method of super-resolution microscopy is provided. The method includes mapping three-dimensional location of each single emission event in a plurality of single emission events from a series of optical images of a sample to create a point cloud. The point cloud is filtered or refined. Clusters within the point cloud are identified and characterized allowing for an assessment of molecular architecture.

Abstract: Systems and methods for training a machine learning model for generating a structural representation of a plant are provided, as well as systems and methods for generating a structural representation of a plant via such a model. The training method involves encoding a plant image into a structural representation of the plant (e.g. a “skeleton”), decoding the structural representation of the plant into a reconstructed image of the plant, and classifying the reconstructed image as having been generated based on a ground-truth structural representation or output of the encoder. Such classification incentivizes the encoder to produce structural representations which do not “smuggle” texture information (e.g. appearance, such as color). Texture information may be separately represented. The encoder, once trained, may be used to generate structural representations from plant images without necessarily requiring decoding or classification.

Abstract: A method of super-resolution microscopy is provided. The method includes mapping three-dimensional location of each single emission event in a plurality of single emission events from a series of optical images of a sample to create a point cloud. The point cloud is filtered or refined. Clusters within the point cloud are identified and characterized allowing for an assessment of molecular architecture.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: Methods and systems for interactively segmenting 3D image data are provided. An initial segmentation of the 3D image data is obtained, and for each of a plurality of image regions, a segmentation uncertainty indicator for the initial image segmentation is associated with the image region and a strength is assigned to the segmentation uncertainty indicator. A low-confidence region in the 3D image data is identified based at least in part on proximity of the low-confidence region to the image regions and strengths of the corresponding segmentation uncertainty indicators. An optimization routine may be applied to an objective function whose value depends at least in part on proximity of the candidate region to the image regions and the strengths of the corresponding uncertainty indicators to identify the low-confidence region from among a plurality of candidate regions.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: Methods and systems for interactively segmenting 3D image data are provided. An initial segmentation of the 3D image data is obtained, and for each of a plurality of image regions, a segmentation uncertainty indicator for the initial image segmentation is associated with the image region and a strength is assigned to the segmentation uncertainty indicator. A low-confidence region in the 3D image data is identified based at least in part on proximity of the low-confidence region to the image regions and strengths of the corresponding segmentation uncertainty indicators. An optimization routine may be applied to an objective function whose value depends at least in part on proximity of the candidate region to the image regions and the strengths of the corresponding uncertainty indicators to identify the low-confidence region from among a plurality of candidate regions.