S.d. Sight Diagnostics Ltd.

Samsung Electronics Co., Ltd.

Dan Gluck

Joseph Joel Pollak

Yochay Shlomo Eshel

Arnon Houri Yafin

Natalie Lezmy

Sharon Pecker

Amir Zait

Sarah Levy Schreier

Sarah Levy

Omri Govrin

Yonatan Halperin

Daniel Levner

Noam Yorav-Raphael

Noam Yorav-raphael

Sharon Alpert

Tomer Avidor

Peretz Yafin

Itamar Weiss

Apparatus and methods are provided including imaging a blood sample that is a cell suspension deposited in a sample chamber. The cells are allowed to settle in the sample chamber to form a monolayer of cells. At least one microscopic image is acquired of the monolayer of cells using a microscope () while the microscope is focused at a monolayer-depth-level, and a first platelet count of platelets that have settled within the monolayer, is determined. An additional microscopic image of the simple is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level, and a second platelet count of platelets that have not settled within the monolayer is determined. An output is generated based upon the first and second platelet counts. Other applications are also described.

Detecting platelets in a blood sample

Apparatus and methods are described including preparing a blood sample for analysis by depositing the blood sample within a sample chamber (), and placing the sample chamber, with the blood sample deposited therein, within a microscopy unit (). One or more microscopic images of the sample chamber () with the blood sample deposited therein are acquired, using a microscope of the microscopy unit. Based upon the one or more images, an amount of one or more cell types within the sample chamber that had already settled within the sample chamber, prior to acquisition of the one or more microscopic images is determined. A characteristic of the sample is determined, at least partially in response thereto. Other applications are also described.

Accounting for errors in optical measurements

Apparatus and methods are described for use with a blood sample. Using a microscope (), three images of a microscopic imaging field of the blood sample are acquired, each of the images being acquired using respective, different imaging conditions, and the first one of the three images being acquired under violet-light brightfield imaging. Using at least one computer processor (), an artificial color microscopic image of the microscopic imaging field is generated, by mapping the first one of the three images to a red channel of the artificial color microscopic image, mapping a second one of the three images to a second color channel of the artificial color microscopic image, and mapping a third one of the three images to a third color channel of the artificial color microscopic image. Other applications are also described.

Artificial generation of color blood smear image

Apparatus and methods are described including measuring hematocrit within a blood sample, by performing a first measurement on a first portion of the blood sample. Mean corpuscular volume in the blood sample is measured, by performing a second measurement on a second portion of the blood sample, the second portion being diluted with respect to the first portion. A red blood cell count per unit volume within the blood sample is determined by dividing the hematocrit measured within the first portion by the mean corpuscular volume measured within the second portion. Other applications are also described.

Performing optical measurements on a sample

Apparatus and methods are described including placing a first portion of a biological sample into a source sample portion chamber of a sample carrier, and placing a second portion of the biological sample into a diluted sample portion chamber. The second portion of the biological sample is diluted with respect to the first portion of the biological sample. Using an optical measurement device, a bulk-level measurand of the sample that relates to a parameter of the biological sample as a whole is measured, by performing a measurement upon the first portion of the biological sample. Microscopic images of the second portion of the biological sample are acquired. Other applications are also described.

Performing measurements on a sample

Apparatus and methods are described for analyzing a bodily sample. A microscope system acquires one or more microscope images of the bodily sample. A computer processor identifies elements as being candidates of a given entity, in the one or more images. At least one sample-informative feature, relating to a characteristic of the candidates of the given entity in the sample as a whole, is extracted from the one or more images. A characteristic of the sample is determined at least partially based upon the sample-informative feature, and an output is generated in response thereto. Other applications are also described.

Methods and apparatus for analyzing a bodily sample

Apparatus and methods are described for analyzing a bodily sample. One or more microscope images of the bodily sample are acquired. Using at least one computer processor at least one sample-informative feature that is indicative of a characteristic of the bodily sample is extracted from the images. Based upon the sample-informative feature, the computer processor determines that there is a defect associated with the bodily sample, and determines a source of the defect. Other applications are also described.

Detecting a defect within a bodily sample

Apparatus and methods are described including staining a blood sample with one or more stains. A plurality of microscopic images of the stained blood sample are acquired, using a microscope. Staining-quality parameters for respective microscopic images are determined, using a computer processor, the staining-quality parameters being indicative of a quality of the staining within each of the respective microscopic images. An action is performed by the computer processor, based upon the staining-quality parameters of the respective microscopic images. Other applications are also described.

Determining a staining-quality parameter of a blood sample

Apparatus and methods are described including acquiring microscopic images of a blood sample, and identifying deformed red blood cells within the microscopic images. A degree of red blood cell deformity within the sample is determined, at least partially based upon the identified deformed red blood cells. A sample-informative parameter that is indicative of a characteristic of the blood sample as a whole is determined, at least partially based upon the degree of red blood cell deformity within the sample. Other applications are also described.

Determining a degree of red blood cell deformity within a blood sample

Apparatus and methods are described for use with a blood sample (), including measuring hemoglobin concentration within at least a portion () of the blood sample, by performing a first measurement on the blood sample. Mean corpuscular hemoglobin in the blood sample () is measured, by performing a second measurement on the blood sample (). A parameter of the blood sample is determined, based on a relationship between the concentration of hemoglobin and the mean corpuscular hemoglobin. Other applications are also described.

Apparatus and methods for analyzing a bodily sample are described. A microscope system acquires one or more microscope images of the bodily sample. A computer processor identifies, in the one or more images, at least one element as being a candidate of a given entity. The computer processor extracts, from the one or more images, at least one candidate-informative feature associated with the candidate of the given entity, and at least one sample-informative feature that is indicative of a characteristic of the bodily sample as a whole. The computer processor processes the candidate-informative feature in combination with the sample-informative feature, and generates an output upon the output device, in response thereto. Other applications are also described.

Methods and apparatus for detecting an entity in a bodily sample

Apparatus and methods are described including a microscope system () configured to acquire one or more microscope images of a bodily sample, an output device (), and at least one computer processor (). The computer processor identifies, in the one or more images, at least one element as being a pathogen candidate, and extracts, from the one or more images, at least one candidate-informative feature associated with the pathogen candidate. The compute processor extracts, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample. The computer processor classifies a likelihood of the bodily sample being infected with a pathogenic infection, by processing the candidate-informative feature in combination with the sample-informative feature, and generates an output upon the output device, in response thereto. Other applications are also described.

Methods, devices and computer-readable mediums for detecting occlusions which occur due to foreground object movement with respect to a background between first and second successive frames. Occlusion detection may use motion estimation with respect to at least a third frame temporally preceding the first frame. Occlusion detection may be based on one or more assumptions such as: occlusion motion vectors are different than other background motion vectors; occlusion motions are likely to be similar to foreground occluding motion; and/or motion estimation will match an occlusion block with a block belonging to a common background object. Occlusion detection may be combined with motion error detection based on a motion field divergence using a motion vector assigned to the occlusion, e.g., for generating an intermediate frame in frame up rate conversion (FRUC).

Image processing with occlusion and error handling in motion fields

A method for selecting a motion vector includes selecting a candidate block included in a second image that corresponds to a processing block of a first image, computing a first probability for the processing block and the candidate block, selecting a random block for the candidate block, computing a second probability for the processing block and the random block, saving a greater of the first probability and the second probability as a first comparison result, computing a third probability for the processing block, a first neighboring block of the processing block, a matching block in the second image that is matched to the first neighboring block, and a second neighboring block of the matching block, and saving a greater of the first comparison result and the third probability as a second comparison result.

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Kadima, Israel

Dan Gluck