Big Data Breakthroughs

Cellular segmentation is the process of identifying and delineating individual cells in an image or a series of images. It is an important task in various fields such as biology, medicine, and computer vision, as it enables the quantitative analysis of cellular morphology, behavior, and interactions. There are several approaches to cellular segmentation, including thresholding, edge detection, region growing, and machine learning-based methods. Thresholding involves setting a pixel intensity value as a threshold, above which pixels are considered part of a cell, and below which pixels are considered background. Edge detection involves identifying edges or boundaries of cells based on changes in pixel intensity values. Region growing involves grouping neighboring pixels with similar intensity values into regions that correspond to individual cells. Machine learning-based methods involve training a model to recognize and segment cells based on features such as shape, texture, and intensi...

Recently, study object detection, classification, segmentation, OCR, etc of computer vision, and review related papers. Reviewed the paper, "U-Net: Convolutional Networks for Biomedical Image Segmentation", which is a deep learning architecture for image segmentation tasks that consists of a contracting path and an expansive path connected by skip connections to retain fine details. Contracting Path: Convolution Bottleneck: Path from contracting to expansive Expansive Path: Concatenation with the correspondingly cropped feature map from the contracting path There are good articles and reviews about U-net, so I highlighted unpadding, elastic deformation, and normalization in image segmentation. Q1. In the trade-off between model accuracy and training efficiency, the paper suggested using unpadded convolutions and reducing the batch to a single image. Is it possible or okay to use padding in U Net and when we use it, is it efficient? A1. In the U Net architecture, instead of us...