Device for detecting bacteria in a sample

Abstract

Device for detecting bacteria in a sample A device for detecting bacteria in a sample comprises an optical block (4) arranged to produce sets of multispectral images of portions of a sample, obtained by illumination eliciting an autofluorescence response, and by illumination eliciting a reflectance response. These sets of multispectral images are supplied to an analyzer (6) comprising a classifier which produces an image in which each pixel is taken from an identical pixel of the images of the set of multispectral images and is associated with an element identifier indicating whether this pixel is associated with a bacterium or with another element. This image is then used by a computer to determine bacteria detection data. Fig.1

Description

the device (2) further comprising an analyzer (6) comprising a classifier (50) and a calculator (54), the classifier (50) comprising a deep learning neural network arranged to receive the set of multispectral images associated with a portion of the sample (12) and to return an image (52) in which each pixel is associated with an element type identifier chosen from a list comprising at least two elements, one of which designates a bacterium, and the calculator (54) being arranged to determine bacteria detection data (16) from the images returned by the deep learning neural network.[Claim 2] Device according to claim 1, wherein said one or more splitters (38) of the collection optics (34) is further arranged to separate the measurement radiation into a third beam distinct from the first beam and the second beam, this third beam having a portion of the measurement radiation having one or more wavelengths in a third range between 470nm and 1000nm, the measurement optics (36) comprising a third optical sensor (44) for measuring the third beam, the analyzer (6) being further arranged to receive the measurements from the third optical sensor (44) with the measurements from the first optical sensor (40) and the second optical sensor (42) to derive the bacteria detection data (16) therefrom.[Claim 3] Device according to claim 1 or 2, wherein the optical bench (4) further comprises an autofocus (32) disposed between the optical source (26) and the magnification objective (30). [Claim 4] Device according to claim 3, wherein the magnification objective (30) and the autofocus (32) are included in the distribution optics (28) and in the collection optics (34). [Claim 5] Device according to one of the preceding claims, wherein the deep learning neural network of the classifier (50) is a convolutional neural network. [Claim 6] Device according to one of claims 1 to 4, wherein the deep learning neural network of the classifier (50) is a transformer. [Claim 7] Device according to one of claims 1 to 4, in which the classifier (50) comprises at least one convolutional neural network coupled to a transformer. [Claim 8] Device according to one of the preceding claims, wherein the calculator (54) is arranged to determine a ratio between the surface area occupied by pixels associated with a bacteria identifier and a reference surface area, and to calculate the bacteria detection data (16) from this ratio. [Claim 9] Device according to one of the preceding claims, wherein the classifier (50) is arranged to associate a pixel with an identifier with an element type identifier chosen from a list comprising a bacterium, a matrix element, an air bubble, a membrane element or a foreign element. [Claim 10] Device according to claim 8 in combination with claim 9,wherein the calculator (54) is arranged to determine the reference surface from the pixels whose identifiers are associated with a membrane element. [Claim 11] A method for detecting bacteria in a sample comprising the following operations: a) determining one or more sets of multispectral images, each set of multispectral images of a given sample portion comprising images obtained by measuring, in a first wavelength range between 400nm and 414nm and in a second range between 414nm and 490nm, an autofluorescence beam emitted by the given sample portion when it is illuminated by radiation of wavelength substantially equal to 365nm, and on the other hand a reflectance beam of the given sample portion when it is illuminated by radiation of wavelengths in a range between 375nm and 1000nm,b) providing each set of multispectral images to a deep learning neural network to produce an image (52) in which each pixel is associated with an element type identifier chosen from a list comprising at least two elements one of which designates a bacterium, and c) calculating bacteria detection data from at least some of the images of step b). [Claim 12] The method of claim 11, wherein step a) comprises determining a set of multispectral images for a set of non-overlapping sample portions whose union covers the entire surface of the sample.,

Claims

13. The method of claim 11, wherein operation a) is performed for a sample portion, then operation b) is performed for that sample portion, and operations a) and b) are repeated until a measurement end condition comprising exceeding a threshold or determining a set of multispectral images for the entire sample is encountered.

14. A method according to claim 11, wherein operation a) is performed for a selected number of sample portions, then a required number of sample portions is determined by applying operation b) to the resulting multispectral image sets, then operations a) and b) are performed with a stopping condition taking into account the required number of sample portions.

15. A method according to claim 14, wherein the number of sample portions required is re-evaluated each time operations a) and b) are performed, and wherein the stopping condition takes into account the re-evaluated number of sample portions required.

16. Method according to one of claims 11 to 15, in which operation c) comprises determining a ratio between the surface occupied by pixels associated with a bacteria identifier and a reference surface, and calculating the bacteria detection data (16) from this ratio. in [Fig. 7] TVC petri dish (LOG) TVC computed (LOG)

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