Custom semantic segmentation neural network architecture in spirochaete detection application

The kingdom of bacteria is a very diverse group of organisms characterized by high phenotypic variability. This feature is often used in clinical diagnosis. The spirochaetes are a microbes with a characteristic spiral shape of a flagella located within the periplasmic space. Nowadays, there is a high demand for creating a rapid and sensitive method for their detection as many of them performs a high pathogenic risk. Currently used methods lays on combination of clinical examination results, serologic and cultivation methods. There can be also used Polymerase Chain Reaction (PCR) method if needed. Unfortunately this combination can be very time consuming and require a lot of money. This research presents a novel, semantic segmentation neural network architecture designed to quickly create a classification mask, outputting information about the position, shape, and possible affiliation of detected elements. The evaluation method is based on a light microscope imagery and was created to overcome above mentioned problems. Used abstract classes contains erythrocytes, spirochaete and background. The resulted mask can be later mapped to a human-readable form with the inclusion of colors, next to an original image. Such approach allows for semi-automatic recognition of unwanted objects, however still giving the final verdict to the specialist. Developed solution has achieved a high recognition accuracy, while the computer power requirements are kept at a minimum. The proposed solution can help reduce misclassification rates by providing additional data for the doctor and speed up the entire process with the early diagnosis made by a neural network.