Abstract: Disclosed herein is method and system for determining quality of semen sample. Trajectories of objects, identified in each of plurality of image frames of semen sample, are generated by tracking movement of the objects across image frames, and compensating a drift velocity of the semen sample. Further, generated trajectories are classified into sperm and non-sperm trajectories. Finally, total concentration estimate and total motility estimate of the semen sample are computed to generate a semen quality index, which indicates quality of the semen sample. In an embodiment, the method of present disclosure uses a multi-level Convolutional Neural Network (CNN) analysis technique for effectively classifying the object trajectories into sperm and non-sperm objects. Also, since the present method includes estimating and compensating drift velocity in the semen sample, it enhances overall accuracy of motility estimation and semen quality analysis.

Abstract: Disclosed herein is method and system for determining quality of semen sample. Trajectories of objects, identified in each of plurality of image frames of semen sample, are generated by tracking movement of the objects across image frames, and compensating a drift velocity of the semen sample. Further, generated trajectories are classified into sperm and non-sperm trajectories. Finally, total concentration estimate and total motility estimate of the semen sample are computed to generate a semen quality index, which indicates quality of the semen sample. In an embodiment, the method of present disclosure uses a multi-level Convolutional Neural Network (CNN) analysis technique for effectively classifying the object trajectories into sperm and non-sperm objects. Also, since the present method includes estimating and compensating drift velocity in the semen sample, it enhances overall accuracy of motility estimation and semen quality analysis.

Abstract: Disclosed herein is method and system for evaluating semen quality. Plurality of images of stained semen sample are captured and analyzed for eliminating unusable images. Further, visible objects in the images are extracted and classified into sperm objects and non-sperm objects. The sperm objects are further classified into normal/abnormal sperm objects based on morphological characteristics of sperm objects, and a differential count of normal/abnormal sperm objects is determined. Subsequently, an aggregate count of the non-sperm objects is determined. Finally, a sperm quality index, indicative of quality of the semen sample, is computed based on morphological characteristics of sperm objects, differential count of normal/abnormal sperm objects, aggregate count of non-sperm objects and total motility estimate of semen sample.