Various techniques are provided for performing automated full-cell segmentation and labeling in immunofluorescent microscopy. These techniques perform membrane segmentation and nuclear seed detection separate and independently from each other, then combine their results to identify cell boundaries. Some embodiments use texture- and kernel-based image processing to perform the method. In some embodiments, the method for obtaining membrane features disclosed herein can be used in conjunction with or separate from the nuclear features. The results can be used for a variety of purposes, including whole-area cell segmentation in fluorescence-based tissue imaging.

Various techniques are provided for performing automated full-cell segmentation and labeling in immunofluorescent microscopy. These techniques perform membrane segmentation and nuclear seed detection separate and independently from each other, then combine their results to identify cell boundaries. Some embodiments use texture- and kernel-based image processing to perform the method. In some embodiments, the method for obtaining membrane features disclosed herein can be used in conjunction with or separate from the nuclear features. The results can be used for a variety of purposes, including whole-area cell segmentation in fluorescence-based tissue imaging.

A method of determining a coverage of an image by an apparatus including processing circuitry includes executing, by the processing circuitry, instructions that cause the apparatus to generate a first segmentation mask by segmenting an image, generate a modified mask by applying a morphological operation to the first segmentation mask, generate a modified masked input based on the image and an inversion of the modified mask, generate a second segmentation mask by segmenting the modified masked input, and determine a coverage of the image based on the first segmentation mask and the second segmentation mask.

A method of determining a coverage of an image by an apparatus including processing circuitry includes executing, by the processing circuitry, instructions that cause the apparatus to generate a first segmentation mask by segmenting an image, generate a modified mask by applying a morphological operation to the first segmentation mask, generate a modified masked input based on the image and an inversion of the modified mask, generate a second segmentation mask by segmenting the modified masked input, and determine a coverage of the image based on the first segmentation mask and the second segmentation mask.

Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced synthetic Shallow Depth of Field (SDOF) images, e.g., by using information from images captured by image camera devices and one or more Deep Neural Networks (DNNs) trained to determine how much blurring should be applied to pixels in a mask region (e.g., a region of pixels having an indeterminate foreground or background status and threshold level of gradient magnitude) within an image, given context from surrounding pixels in a reference image and/or an unenhanced synthetic SDOF image. To train the DNNs, various sets of ground truth DSLR images of static scenes, captured at varying aperture settings, may be analyzed. Preferably, such static scenes cover many examples of human subjects (or other objects of interest) with different amounts of scene foreground/background separation, lighting conditions, and various kinds of hair, fabric, or other fine-grained details occurring near their foreground/background transition.

Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced synthetic Shallow Depth of Field (SDOF) images, e.g., by using information from images captured by image camera devices and one or more Deep Neural Networks (DNNs) trained to determine how much blurring should be applied to pixels in a mask region (e.g., a region of pixels having an indeterminate foreground or background status and threshold level of gradient magnitude) within an image, given context from surrounding pixels in a reference image and/or an unenhanced synthetic SDOF image. To train the DNNs, various sets of ground truth DSLR images of static scenes, captured at varying aperture settings, may be analyzed. Preferably, such static scenes cover many examples of human subjects (or other objects of interest) with different amounts of scene foreground/background separation, lighting conditions, and various kinds of hair, fabric, or other fine-grained details occurring near their foreground/background transition.

A method of operating an image processing apparatus is provided. The method includes generating a first feature map by performing a convolution operation between a first image and a first kernel group, generating a second feature map by performing a convolution operation between the first image and a second kernel group, generating a first combination map based on the first feature map, generating a second combination map based on the first feature map and the second feature map, generating a second image based on the first combination map and the second combination map, and generating a reconstructed image of the first image, based on the second image and the first image, and generating a high-resolution image of the first image by inputting the reconstructed image to an upscaling model.

A method of operating an image processing apparatus is provided. The method includes generating a first feature map by performing a convolution operation between a first image and a first kernel group, generating a second feature map by performing a convolution operation between the first image and a second kernel group, generating a first combination map based on the first feature map, generating a second combination map based on the first feature map and the second feature map, generating a second image based on the first combination map and the second combination map, and generating a reconstructed image of the first image, based on the second image and the first image, and generating a high-resolution image of the first image by inputting the reconstructed image to an upscaling model.

A method for building a map includes: acquiring an original grayscale map, preprocessing the original grayscale map to obtain a preprocessed map, binarizing the preprocessed map to obtain a binarized map, performing a boundary filling to the preprocessed map and the binarized map to obtain a boundary-filled preprocessed map and a boundary-filled binarized map, performing a boundary thinning to the boundary-filled binarized map to obtain a thinned binarized map, and performing a boundary thinning to the boundary-filled preprocessed map, according to the thinned binarized map, to obtain a thinned preprocessed map.

The disclosure provides a theseometer or proprioceptometer for objectively quantifying the proprioceptive performance of a subject such as a human. The disclosed theseometer is a device comprising a clear, rigid material or screen having or exhibiting a distinguishable target embraced by a series of concentric rings, a digital camera with a lens concentric to the target, a base unit comprising an electronic processor and memory for analyzing data and, optionally, a wheeled base to provide mobility and portability.