Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages

Anonymous

Keywords: Deep learning, Bioimages, Landmark detection, Heatmap, Multi-variate regression

TL;DR: This paper evaluate deep learning methods for the problem of landmark detection in Bio-images

Abstract: In this paper we perform an empirical evaluation of variants of deep learning methods to automatically localize anatomical landmarks in bioimages of fishes acquired using different imaging modalities (microscopy and radiography). We compare two methodologies namely heatmap based regression and multivariate direct regression, and evaluate them in combination with several Convolutional Neural Network (CNN) architectures. Heatmap based regression approaches employ Gaussian or Exponential heatmap generation functions combined with CNNs to output the heatmaps corresponding to landmark locations whereas direct regression approaches output directly the $(x,y)$ coordinates corresponding to landmark locations. In our experiments, we use two microscopy datasets of Zebrafish and Medaka fish and one radiography dataset of gilthead Seabream. On our three datasets, the heatmap approach with Exponential function and U-Net architecture performs better. Datasets and open-source code for training and prediction will be published (upon paper acceptance) to ease future landmark detection research and bioimaging applications.