Abstract: Methods and systems for automated assessment of spermatogenesis. Embodiments disclosed herein relate to drug development and testicular toxicity in safety evaluation studies, and more particularly to automatic assessment of spermatogenesis through a staging of seminiferous tubules using Artificial Intelligence/deep learning methods. A method disclosed herein includes detecting the seminiferous tubules by analyzing a testes tissue specimen and mapping the seminiferous tubules to detect and segment germ cells. The method includes classifying the seminiferous tubules into respective stages based on the segmented germ cells. The method further includes categorizing the seminiferous tubules into a normal category and an abnormal category based on the segmented germ cells. The method further includes categorizing the testes tissue specimen into the normal category and the abnormal category based on the classification of the seminiferous tubules for toxicity analysis.

Abstract: Embodiments herein relate to prostate cancer management, and more particularly to managing prostate cancer by obtaining fine-grained Gleason grades and predicting clinical outcomes from needle core biopsy images and radical prostatectomy images. A deep learning model is fed with a plurality of training images and is trained to recognize fine-grained Gleason patterns in the plurality of training images. The output of the deep learning model is representative of the fine-grained Gleason score.

Abstract: Micro-biological colony counters and more particularly, to a device and a method for lighting, conditioning and capturing image(s) of organic sample(s) such as but not limited to micro-organisms. The device (700) captures accurate image(s) of organic sample(s) and has a fixed focus imaging for repeatability in quality of images. The device (700) can capture images of organic sample in different lighting and color conditions thereby improving detection of microbiological colonies by increasing the contrast from the background medium. The color calibrated imaging device (700) provides diffused illumination by using polychromatic LED lights, light reflectors and light diffusers for optimal color reproduction of micro-biological colonies contained in organic sample(s). The device (700) is adapted for automatic capturing of images of organic sample(s) cultivated on petri dishes of different sizes.

Abstract: Micro-biological colony counters and more particularly, systems and methods for feeding, identifying, counting, classifying, segregating and collecting organic samples such as but not limited to micro-organisms. The system can classify colonies in different classes such as bacteria and fungus present in an organic sample and also gives a digital count on the number of colonies present in each of the classes separately. The system reduces the time in counting and classification of microbes in each class separately, eliminates manual errors and requires less manual intervention. The system is reliable and can perform the counting and classification of microbes in each class separately even in absence of well-trained technician. The system can count surface colonies in petri-dish and can count colonies in different size or diameter of petri-dish.

Abstract: Methods and systems for managing a petri-dish. Embodiments herein disclose a RFID tag affixed on the petri-dish, wherein the RFID tag has a thin formfactor, so as not to interfere in the use and operation of the petri-dish and a sufficiently large readability range. Embodiments herein disclose methods and systems for RFID based asset tracking of petri-dishes in a laboratory/pharmaceutical/manufacturing environment, wherein the movement of the petri-dishes are tracked automatically with minimal manual intervention.

Abstract: Methods and systems for automated counting and classifying microorganisms. A method disclosed herein includes receiving and analyzing quality of at least one input media of at least one incubated dish used for growth of the colonies of the microorganisms. The method further includes detecting the colonies of the microorganisms in a growth medium disposed on the dish if the received at least one media is a good quality media, wherein the detected colonies include at least one of individual colonies and grouped colonies. The method further includes segregating the grouped colonies into the individual colonies. The method further includes classifying the individual colonies into at least one species of the microorganisms. The method further includes counting the colonies of each species.