Abstract:
An observation device includes a stage, an imaging optical system that includes an objective lens, a detection section that includes displacement sensors that detect a vertical position of a cultivation container, an imaging optical system controller that controls the imaging optical system driving section to move the objective lens in an optical axis direction on the basis of the vertical position of the cultivation container, and a horizontal driving section that moves the stage in a main scanning direction and a sub-scanning direction and reciprocates the stage in the main scanning direction, in which the detection section detects the vertical position of the cultivation container at a forward position in a movement direction of the observation region with reference to the position of the observation region of the imaging optical system with respect to the cultivation container.
Abstract:
An image processing device which generates a halftone image includes: an image size adjustment unit which adjusts a size of the input image; and a halftone processing unit which performs halftone processing on the input image size-adjusted to generate a halftone image, wherein: the image size adjustment unit adjusts the input image to a same size in two or more printing modes among the plurality of printing modes, and the input image of the same size is subjected to the halftone processing; the unit limits arrangement of dots constituting the halftone image to dot arrangeable places of a selected printing mode among the plurality of printing modes, and performs the halftone processing based on an error diffusion method using an error diffusion coefficient matrix according to the selected printing mode; and the functions performed by the image size adjustment unit and the halftone processing unit are achieved using a computer.
Abstract:
There is provided a mini-batch learning apparatus that learns a machine learning model for performing semantic segmentation, which determines a plurality of classes in an image in units of pixels, by inputting mini-batch data to the machine learning model, the apparatus including a calculation unit, a specifying unit, and a generation unit. The calculation unit calculates, from a learning input image and an annotation image which are sources of the mini-batch data, a first area ratio of each of the plurality of classes with respect to an entire area of the annotation image. The specifying unit specifies a rare class of which the first area ratio is lower than a first setting value. The generation unit generates the mini-batch data from the learning input image and the annotation image. The generation unit generates the mini-batch data in which a second area ratio of the rare class is equal to or higher than a second setting value higher than the first area ratio calculated by the calculation unit.
Abstract:
In a case where the operation program is started, a CPU of the mini-batch learning apparatus functions as a calculation unit, a specifying unit, and an evaluation unit. The calculation unit calculates an area ratio of each of a plurality of classes in mini-batch data. The specifying unit specifies, as a correction target class, a rare class of which the area ratio is lower than a setting value. The evaluation unit evaluates the class determination accuracy of the machine learning model by using a loss function. As correction processing, the evaluation unit sets a weight for a loss value of the rare class to be larger than a weight for a loss value of a class other than the rare class.
Abstract:
The microscope apparatus includes: an illumination light irradiation unit that irradiates an object to be observed with illumination light for phase-contrast measurement; a transmission type display unit on which light, which is transmitted through the object to be observed due to irradiation with the illumination light for phase-contrast measurement, is incident; and an imaging unit that images the object to be observed by detecting the light transmitted through the transmission type display unit. The transmission type display unit displays patterns where transmission density varies for every light having a plurality of wavelengths included in the light transmitted through the object to be observed.
Abstract:
Disclosed is a method for manufacturing a conductive film in which a mesh pattern comprising a wire material is provided on a base material. Also disclosed are a conductive film and a recording medium. Image data representing a mesh pattern is created on the basis of a plurality of selected positions. On the basis of said image data, an evaluation value which quantifies noise characteristics of the mesh pattern is computed. On the basis of the computed evaluation value and prescribed evaluation conditions, one image datum is chosen as an output image datum.
Abstract:
A cell culture evaluation device includes at least one processor. The processor is configured to acquire a cell image obtained by imaging a cell that is being cultured, to input the cell image to an image machine learning model and output an image feature amount set composed of a plurality of types of image feature amounts related to the cell image from the image machine learning model; and to input the image feature amount set to a data machine learning model and output an expression level set composed of expression levels of a plurality of types of ribonucleic acids of the cell from the data machine learning model.
Abstract:
Provided are an imaging device, an imaging method and an imaging control program which make it possible to shorten an imaging time, and to capture an image of each observation region at an appropriate focusing position regardless of the state of installation of a culture vessel on a stage. In a case where an observation target is imaged multiple times, a focusing position of an observation region within a culture vessel is detected by auto-focus control during first imaging, and the first imaging of each observation region is performed using the focusing position. Next, the focusing position of a reference position is detected by auto-focus control during second imaging subsequent to the first imaging, and the focusing position of each observation region detected in the first imaging is corrected on the basis of the detected focusing position and the focusing position of the reference position in the first imaging.
Abstract:
A cell culture device and a cell culture method that enable living tissues for transplantation having constant qualities to be stably supplied as products are provided. A plurality of cell culture portions that respectively culture a plurality of cell culture units; a maturity evaluation portion that respectively evaluates maturity of the respective cell culture units in the respective cell culture portions and a movement target cell determining portion that determines a cell culture unit that does not satisfy a predetermined condition as a target to be moved to another cell culture portion in a case where the cell culture unit of which maturity does not satisfy the predetermined condition among a plurality of cell culture units cultured in any one cell culture portion of the plurality of cell culture portions exists are provided.
Abstract:
There is provided a cell imaging apparatus and method capable of generating a high-quality composite image as an image to be evaluated. The cell imaging apparatus includes: an imaging unit 10 that images a cell group including a plurality of periodically moving cells while changing an imaging range; a phase information acquisition unit 21 that acquires information based on the timing of the same phase in each period of the periodic movement; and a composite image generation unit 22 that generates a composite image by arranging images of the respective imaging ranges. The imaging unit 10 captures images with the same phase for the respective imaging ranges on the basis of the information based on the timing of the same phase, and the composite image generation unit 22 generates the composite image by arranging the images with the same phase.