Abstract:
An image processing apparatus includes: an image acquiring unit configured to acquire image information representing an image acquired by irradiating a gland duct with excitation light and observing fluorescence generated in the gland duct; a fluorescence intensity computation unit configured to compute a value corresponding to intensity of the fluorescence as fluorescence intensity based on the image information; and an image determination unit configured to determine whether or not an endocrine cell exists in the gland duct based on the fluorescence intensity computed by the fluorescence intensity computation unit, and to determine abnormality of the gland duct based on a determination result of the endocrine cell.
Abstract:
An information-processing apparatus includes a display displaying an image; a memory recording voice data having a voice pronounced at each of plural observation points of the image; a gaze detector generating gaze data by detecting a gaze of a user; a voice input device generating voice data associated with a time axis identical to that of the gaze data by receiving a voice of the user; and a processor to analyze a attention period where a attention degree of the gaze to each of the plural observation points is a predetermined value or greater, based on the gaze data, set a period where the voice is pronounced with respect to the voice data as an important voice period, based on the voice data, and generate calibration data based on a time lag between the attention period and the important voice period.
Abstract:
An image processing device includes a processor including hardware. The processor is configured to: sequentially perform, on all at least two types of stains, an extraction process to extract a stained area due to a target stain from a single stain image of the target stain, starting from a stain having high specificity with regard to a target site; and sequentially perform a correction process, on all at least one type of second stain except for a first stain having the highest specificity among the at least two types of stains, to correct the single stain image by excluding stained areas of all stains having higher specificity than the target stain from the single stain image of the target stain, starting from the stain having the high specificity.
Abstract:
An image processing apparatus includes: an image acquiring unit configured to acquire image information representing an image acquired by irradiating a gland duct with excitation light and observing fluorescence generated in the gland duct; a fluorescence intensity computation unit configured to compute a value corresponding to intensity of the fluorescence as fluorescence intensity based on the image information; and an image determination unit configured to determine whether or not an endocrine cell exists in the gland duct based on the fluorescence intensity computed by the fluorescence intensity computation unit, and to determine abnormality of the gland duct based on a determination result of the endocrine cell.
Abstract:
A learning model preparation method includes: acquiring a non-specific binding specimen image representing a non-specific binding specimen that allows a reagent to act on a tissue containing the endogenous protein, the reagent developing a color in a non-specific binding region in which an endogenous protein exists; and preparing a first learning model by setting the non-specific binding specimen image to first learning data, and by allowing a learning device to learn the non-specific binding region, based on the first learning data.
Abstract:
An image processing apparatus includes: a fluorescence intensity information storage configured to store therein fluorescence intensity information and fluorescence intensity information; and a processor including hardware. The processor is configured to: calculate intensities of fluorescence in a plurality of fluorescence images; create a three-dimensional fluorescence intensity distribution of the same living body from the calculated intensities of fluorescence of the plurality of fluorescence images; identify, by using the created fluorescence intensity distribution, body tissue that is matched with the fluorescence intensity information on the predetermined body tissue read from the fluorescence intensity information storage; identify, by using the created fluorescence intensity distribution, for each of pieces of the identified body tissue, a cell that is matched with the fluorescence intensity information on the detection target cell read from the fluorescence intensity information storage; and determine a state of the identified body tissue based on information on the identified cell.
Abstract:
An image processing apparatus includes an image acquisition unit that acquires image information representing a fluorescence observation image of a specimen stained with hematoxylin-eosin, a spectrum generation unit that generates a plurality of spectra each representing a wavelength distribution of fluorescence intensity in a plurality of pixels in the fluorescence observation image, a pixel extraction unit that extracts at least two pixel groups with a feature of a particular spectrum from the plurality of pixels, and an image generation unit that generates an image based on the extracted pixel groups.
Abstract:
An information processing apparatus is disclosed which includes a hardware processor configured to, analyze an attention degree of a gaze of a user, on the basis of gaze data in which the gaze of the user is detected, the gaze data being input externally, and assign an important degree according to the attention degree that is analyzed with respect to speech data of the user, the speech data being input externally, and is associated with a time axis that is a same as that of the gaze data to be recorded in a memory.
Abstract:
An image processing device includes a processor including hardware. The processor is configured to: sequentially perform, on all at least two types of stains, an extraction process to extract a stained area due to a target stain from a single stain image of the target stain, starting from a stain having high specificity with regard to a target site; and sequentially perform a correction process, on all at least one type of second stain except for a first stain having the highest specificity among the at least two types of stains, to correct the single stain image by excluding stained areas of all stains having higher specificity than the target stain from the single stain image of the target stain, starting from the stain having the high specificity.
Abstract:
An image processing apparatus includes an image input unit configured to input a plurality of images acquired by imaging a specimen stained with non-fluorescent dye at a plurality of wavelength bands that are different from one another and a characteristic amount calculation unit configured to calculate a characteristic amount representing auto-fluorescence emitted by the specimen based on the plurality of images.