Systematic characterization of objects in a biological sample

A computer-implemented method for classifying and counting objects recoverable from a urine sample processed onto a slide, said method comprising:

• receiving at least one digitalized image of the whole slide;
• detecting connected components by segmentation of the image of the whole slide;
• classifying the detected connected components into countable connected components and uncountable connected components using a classifier;
• for the countable connected components:
• inputting each countable connected component into an object detection model so as to detect objects and obtain an output comprising a bounding box and an associated class for each detected object;
• counting the bounding boxes associated to each class obtaining a number of objects for each class;
• for the uncountable components:
• inputting each uncountable connected component into a semantic segmentation model and obtaining as output a segmentation mask in which all pixels are classified into one class among multiple predefined available classes;
• for each class, counting the number of objects as the ratio between a total pixel's area of the class, obtained as the number of pixels of the segmentation mask associate to said class, and an average area of the object of said class;
• summing up the number of objects for each class obtained from the semantic segmentation model and the object detection model;
• outputting the number of objects for each class;
• wherein said classes for the semantic segmentation model and the object detection model are the same.

A system for classifying and counting objects recoverable from a urine sample processed onto a slide, said system comprising:

• at least one input adapted to receive at least one digitalized image of the whole slide comprising multiple objects;
• at least one processor configured to:
• detect connected components by segmentation of the image of the whole slide;
• classify the detected connected components into countable connected components and uncountable connected components using a classifier;
• for the countable connected components:
• (i) input each countable connected component into an object detection model configured to detect objects and to output for each object one bounding box and an associated class;
• (ii) count the bounding boxes associated to each class obtaining the number of objects for each class;
• for the uncountable components:
• (i) input each uncountable connected component into a semantic segmentation model and obtain as output a segmentation mask in which all pixels are classified into one class among the predefined available classes;
• (ii) for each class, count the number of objects as the ratio between a total pixel's area of the class, obtained as the number of pixels of the segmentation mask associate to said class, and an average area of the object of said class;
• summing up the number of objects for each class obtained from the semantic segmentation model and the object detection model;
• wherein said classes for the semantic segmentation model and the object detection model are the same;
• and
• at least one output adapted to provide a number of objects for each class.

A computer program product for classifying and counting objects recoverable from a urine sample, the computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method comprising:

• receiving at least one digitalized image of the whole slide;
• detecting connected components by segmentation of the image of the whole slide;
• classifying the detected connected components into countable connected components and uncountable connected components using a classifier;
• for the countable connected components:
• inputting each countable connected component into an object detection model so as to detect objects and obtain an output comprising a bounding box and an associated class for each detected object;
• counting the bounding boxes associated to each class obtaining a number of objects for each class;
• for the uncountable components:
• inputting each uncountable connected component into a semantic segmentation model and obtaining as output a segmentation mask in which all pixels are classified into one class among multiple predefined available classes;
• for each class, counting the number of objects as the ratio between a total pixel's area of the class, obtained as the number of pixels of the segmentation mask associate to said class, and an average area of the object of said class;
• summing up the number of objects for each class obtained from the semantic segmentation model and the object detection model;
• outputting the number of objects for each class;
• wherein said classes for the semantic segmentation model and the object detection model are the same.

A non-transitory computer-readable storage medium comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method comprising

• receiving at least one digitalized image of the whole slide;
• detecting connected components by segmentation of the image of the whole slide;
• classifying the detected connected components into countable connected components and uncountable connected components using a classifier;
• for the countable connected components:
• inputting each countable connected component into an object detection model so as to detect objects and obtain an output comprising a bounding box and an associated class for each detected object;
• counting the bounding boxes associated to each class obtaining a number of objects for each class;
• for the uncountable components:
• inputting each uncountable connected component into a semantic segmentation model and obtaining as output a segmentation mask in which all pixels are classified into one class among multiple predefined available classes;
• for each class, counting the number of objects as the ratio between a total pixel's area of the class, obtained as the number of pixels of the segmentation mask associate to said class, and an average area of the object of said class;
• summing up the number of objects for each class obtained from the semantic segmentation model and the object detection model;
• outputting the number of objects for each class;
• wherein said classes for the semantic segmentation model and the object detection model are the same.