An Extended Type Cell Detection and Counting Method based on FCN

Cell detection and counting are critical and essential tasks for many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming and prone to induce subjective bias. These make automatic cell counting and detection essential for large- scale and objective studies. Unfortunately, the hard examples such as cell blur, clutter, bleed-through and imaging noise make these tasks extremely challenging. Over the last few years, automatic cell detection and counting have evolved from earlier methods that are often based on filters to the current state-of- the-art deep learning methods. In this paper, we propose a novel efficient method for robust counting and detection task based on fully convolution networks (FCN). Our method is able to handle most of detection and counting problems from different kinds of cell datasets, and can cover most senior microscopy images, such as bright field, pathology stained material and electron. Extensive experiments on the public and private datasets demonstrate the effectiveness and reliability of our approach.

I. Introduction

Objects detection and counting in real world and biological applications from crowded scene (videos, images and microscopies) are extremely time consuming and tedious task [1]. Cells detection in microscopy images is an important and highest priority task for many biological studies, including cancer region and cells detection in pathological images, high content and high throughput gene and protein expression analysis, cell morphology researches and molecular markers and phenotypes quantification [2].