The mitotic index (MI) is an important measure in cell proliferation studies. Determination of the MI is usually made by light-microscope analysis of slide preparations. The analyst identifies and counts thousands of cells and reports the percentage of mitotic shapes found among the interphase nuclei. Full automation of this process is an ambitious task, because there can exist very few mitotic shapes among hundreds of nuclei and thousands of artifacts, resulting in a high probability of false positives, i.e. objects erroneously identified as mitosis or nuclei. A semi-automated approach for MI calculation is reported, based on the development of a neural network (NN) for automatic identification of metaphase spreads and stimulated nuclei in digital images of microscope preparations at 10X magnification. After segmentation of the objects on each image, ten different morphometrical, photometrical and textural features are measured on each segmented object. An NN is used to classify the feature vectors into three classes: metaphases, nuclei and artifacts. The system has been able to classify correctly approximately 91% of the objects in each class, in a test set of 191 mitosis, 331 nuclei and 387 artifacts, obtained from 30 different microscope slides. Manual editing of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.

Abstract-In this paper is reported the development of a neural network (NN) based workstation for automated cell proliferation analysis, of cytological microscope images. The software of the system assists the expert biotechnologist during cell proliferation and chromosome aberration studies by automatically identifying metaphase spreads and stimulated nuclei on each digital image. After manual edition of metaphase false positives, the system automatically calculates the mitotic index (MI) i.e. the ratio of metaphases to stimulated nuclei of a given tissue sample. The system reported has been able to classify correctly approximately 91% of the metaphases and stimulated nuclei, in a test set of 191 mitosis, 331 nuclei, and 387 artefacts, obtained from 30 different microscope slides. Manual edition of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.

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Human infertility is considered a serious disease of the reproductive system that affects more than 10% of couples worldwide, and more than 30% of reported cases are related to men. The crucial step in evaluating male infertility is a semen analysis, highly dependent on sperm morphology. However, this analysis is done at the laboratory manually and depends mainly on the doctor's experience. Besides, it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net), which focuses on the segmentation of sperm cells in micrographs to overcome these problems. The results showed high scores for the model segmentation metrics such as precision (93%), IoU score (86%), and DICE score of 93%. Moreover, we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells.