Cervical cancer is one of the main causes of death by disease worldwide. In Peru, it holds the first place in frequency and represents 8% of deaths caused by sickness. To detect the disease in the early stages, one of the most used screening tests is the cervix Papanicolaou test. Currently, digital images are increasingly being used to improve Pap test efficiency. This work develops an algorithm based on adaptive thresholds, which will be used in Pap smear assisted quality control software. The first stage of the method is a pre-processing step, in which noise and background removal is done. Next, a block is segmented for each one of the points selected as not background, and a local threshold per block is calculated to search for cell nuclei. If a nucleus is detected, an artifact rejection follows, where only cell nuclei and inflammatory cells are left for the doctors to interpret. The method was validated with a set of 55 images containing 2317 cells. The algorithm successfully recognized 92.3% of the total nuclei in all images collected. / Revisón por pares

<p>Recent advances in fluorescence  microscopy enable deeper cellular imaging in living tissues with near-infrared excitation light. </p> <p>High quality fluorescence microscopy images provide useful information for analyzing biological structures and diagnosing diseases.</p> <p>Nuclei detection and segmentation are two fundamental steps for quantitative analysis of microscopy images.</p> <p>However, existing machine learning-based approaches are hampered by three main challenges: (1) Hand annotated ground truth is difficult to obtain especially for 3D volumes, (2) Most of the object detection methods work only on 2D images and are difficult to extend to 3D volumes, (3) Segmentation-based approaches typically cannot distinguish different object instances without proper post-processing steps.</p> <p>In this thesis, we propose various new methods for microscopy image analysis including nuclei synthesis, detection, and segmentation. </p> <p>Due to the limitation of manually annotated ground truth masks, we first describe how we generate 2D/3D synthetic microscopy images using SpCycleGAN and use them as a data augmentation technique for our detection and segmentation networks.</p> <p>For nuclei detection, we describe our RCNN-SliceNet for nuclei counting and centroid detection using slice-and-cluster strategy. </p> <p>Then we introduce our 3D CentroidNet for nuclei centroid estimation using vector flow voting mechanism which does not require any post-processing steps.</p> <p>For nuclei segmentation, we first describe our EMR-CNN for nuclei instance segmentation using ensemble learning and slice fusion strategy.</p> <p>Then we present the 3D Nuclei Instance Segmentation Network (NISNet3D) for nuclei instance segmentation using gradient vector field array.</p> <p>Extensive experiments have been conducted on a variety of challenging microscopy volumes to demonstrate that our approach can accurately detect and segment the cell nuclei and outperforms other compared methods.</p> <p>Finally, we describe the Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system we developed for biologists to remotely analyze large microscopy volumes using machine learning. </p>