EP-4735573-A1 - METHOD FOR DETECTING COLONIES OF MICROORGANISMS IN A SAMPLE ARRANGED IN A CULTURE MEDIUM

Abstract

The invention relates to a method for detecting colonies of microorganisms in a biological sample arranged in a solid culture medium, the method being implemented by a detection system comprising at least one incubator, an analysis unit, and at least one automated image capture system, the method comprising a first phase of detecting and counting objects, in the field of view of the image capture system, exhibiting growth, and a second phase applied only to those samples deemed to be negative in the first phase of the method, the second phase being based on anomaly detection applied to at least the last image acquired in the incubation sequence.

Inventors

• LALOUM, Eric

Assignees

• bioMérieux

Dates

Publication Date

20260506

Application Date

20240618

Claims (1)

1. CLAIMS 1. Method for detecting colonies of microorganisms in a sample deposited in a solid culture medium, said method being implemented by a detection system comprising at least one incubator, an analysis unit, and at least one automated image capture system, said method comprising a first phase (PI) of detecting and counting objects, in the field of view of the image capture system, exhibiting growth, the first phase (PI) comprising at least the steps according to which: (P101) Acquisition of a plurality of images of the culture medium in the field of view of the image capture system, during incubation of said sample in the incubator, by the image capture system, (P102) Detection of objects in images acquired by segmentation with a thresholding method; Extraction on said detected objects of a parameter of growth of size of the object between successive images, Determination of the presence or absence of microorganism(s) by detection of growth of said at least one object by the analysis unit, (i) if growth of the object is detected, then the analysis unit considers that there is the presence of at least one colony of microorganisms in the sample, the sample is said to be positive; (ii) if no growth of the object is detected, then the analysis unit considers that there is an absence of a colony of microorganisms in the sample, the sample is said to be negative, (P103) Counting said objects exhibiting growth by the analysis unit, characterized in that the method further comprises a second phase (P2) applied only to the samples considered negative in the first phase of the method, said second phase (P2) being based on an anomaly detection applied to at least the last image acquired during the incubation sequence, and comprising the following steps: (P201) Detection of objects in the acquired image by segmentation with a thresholding method; (P202) Extraction of position and/or size and/or morphology and/or intensity parameters from said detected objects, (P203) Application of a discrimination model estimated from a learning database contained in the analysis unit for example and based on a score calculated from all the parameters extracted in the extraction step in order to separate anomalies from artifacts. 2. Detection method according to claim 1, in which the second phase may comprise one or more additional pre-processing steps such as the fusion of different contrast modalities, the conversion into gray levels, the convolution filtering, the definition of a region of interest. 3. Detection method according to any one of claims 1 or 2, in which the morphological parameter may be the convexity of the detected object and/or the circularity of the detected object. 4. Detection method according to any one of claims 1 to 3, in which the morphological classification parameters are less strict than those of position. 5. Detection method according to any one of claims 1 to 4, in which the size parameter can be the diameter and/or the perimeter and/or the area of the detected object. 6. Detection method according to any one of claims 1 to 5, in which the intensity parameter can be the average gray levels of the detected object and/or the variation of the intra-object gray level. 7. Detection method according to any one of claims 1 to 6, in which the score calculated from all the extracted parameters, allowing the classification of the object, can be calculated from one of the methods: regression, decision tree, linear discriminant analysis, Support Vector Machine, Neural Networks. 8. Detection method according to any one of claims 1 to 7, in which from the different criteria calculated on the extracted objects, the discrimination model is therefore estimated from a restricted and specific learning base containing only the colonies not detected by the first phase and all possible artifacts. 9. Detection method according to any one of claims 1 to 8, in which the second phase (P2) further comprises a step of merging the different contrast modalities, and a step of conversion into gray levels, for example on 8 bits in 256 levels, these steps being carried out before the step of segmenting the objects. 10. Detection method according to any one of the preceding claims, in which the parameterization of the first phase is optimized on a database of nominal cases by excluding problematic cases. 11. Detection method according to any one of the preceding claims, in which the second phase further comprises a step of pre-processing the last acquired image involving another image of the sequence, for example the first acquired image.

Description

Method for detecting colonies of microorganisms in a sample placed in a culture medium Technical field of the invention The invention finds its application in particular in the field of microbiological control of sterile products and their production environment. The invention relates to the field of contamination detection systems on Petri dishes or other culture media, preferably solid, to be incubated. More particularly, the invention relates to the field of colony forming unit (CFU) detection systems on Petri dishes and based on sequential and automated reading of said Petri dishes throughout the incubation period. Technological background of the invention Environmental microbiological monitoring of production areas is regularly carried out in the pharmaceutical field, particularly for grade A and B controlled aseptic production areas. Traditionally, sedimentation boxes are used for air analysis or so-called "contact" boxes for surface analysis, the incubation time of which varies between 5 and 7 days and the reading of the result (contamination or not) is carried out after incubation by an operator. This type of monitoring of aseptic areas poses specific constraints insofar as: i. several hundred Petri dishes containing solid culture medium can be recovered each day depending on the defined control plan and regulatory requirements, ii.most samples collected in these areas are negative (between 95 and 99.9%), iii.more than the exact number of colonies growing on solid culture medium, it is the qualitative status of the sample (negative for CFU=0 and positive for CFU>0), which is monitored, as recommended in the regulatory texts ("Contamination Recovery Rate defined in USP <1116>). In the event of contamination, an investigation is carried out to identify the contaminating microorganism(s) in order to know their species and/or strain and thus trace the causes of the contamination. This traditional method is relatively long, tedious, difficult to trace and dependent on the skills of the operator (training, visual ability, etc.). Even though it is considered the reference, the performance of this method is not perfect and there remains a false negative rate estimated at around 2%. To make this method more efficient and traceable, without degrading its performance, automated reading systems based on digital image capture and associated processing have been developed. On the one hand, there are classic colony reading systems, called "end point" where human reading is replaced by recording and automated processing of images taken on the Petri dish at the end of incubation. These systems are well suited for enumeration applications, when the microbial load (quantity of microorganism in the sample) is generally significant (a few dozen or even hundreds of colony-forming units) and it is not a question of performing a qualitative presence/absence test. However, in the case of qualitative contamination detection applications, the performances are generally not as good as those of the traditional method in terms of sensitivity (ability to correctly detect a colony when it is present) and specificity (ability to not trigger a false alarm). There are indeed many artifacts on the Petri dish (dust, marks, defects on the surface of the agar, ...) that are wrongly recognized as colonies, regardless of the quality of the optical capture system, the image processing or the algorithm used. There is a trade-off between false positives and false negatives inherent in the acquired basic information not compatible with the requirements of the qualitative contamination test application, as illustrated in Figure 1. To overcome the limitations of automatic "end-point" reading systems and to consider equivalence with the performance of the traditional method, particularly for environmental microbiological control of aseptic production areas, a new type of system has been developed, called "continuous reading", integrating an incubator, and based on automated reading, by taking images throughout the incubation and processing the sequences thus obtained. These systems read from closed Petri dishes, to avoid cross-contamination inside the incubator that contains multiple Petri dishes, and generally integrate multiple contrast sources (transmission, diffuse reflection, grazing illumination, etc.) in order to capture the maximum amount of information and to compensate for possible reading biases due to the presence of the lid (which can be removed in the traditional method of reading at the end of incubation). In these systems, successive images of the Petri dish are recorded during its incubation, generally at regular intervals (for example, a image every hour) and we proceed to their analysis as the acquisition progresses, with image processing operations and a generic protocol as described in figure 2. These systems integrate an image processing chain with a few critical steps, such as the normalization of intensities (in order to