The first step in most quantitative analyses of biological image data is segmentation, which involves labelling or annotating objects of interest. In this webinar, you will learn how to use Biomedisa in combination with 3D Slicer, both freely available open-source platforms for biological and medical image analysis. We will begin by using Biomedisa’s smart interpolation to semi-automatically create training data. This data will then be used to train Biomedisa’s deep neural network for automated segmentation, demonstrated through example cases such as mouse molar teeth from micro-CT scans and mitochondria in electron microscopy images.
Dr Philipp Loesel is postdoctoral research fellow at the Australian National University CTLab. He specialises in biological and medical image analysis, focusing on AI-based segmentation of large 3D volumetric image data, such as those generated by X-ray computed tomography and electron microscopy. He studied Mathematics and Political Science at Heidelberg University, Germany. Following his studies he served as a lecturer in Mathematics and Informatics for Molecular Biotechnology at Heidelberg University. In 2022, he completed his PhD at Heidelberg University, where he developed the online segmentation platform Biomedisa. He is engaged in several interdisciplinary research projects, spanning medicine (such as incisional hernia repair), biology (such as segmentation of plant roots in soil), and geology (such as rock particle segmentation), with a focus on interactive and active learning techniques.
This webinar is presented by Volume Imaging Australia, a special interest group of the Australian Microscopy and Microanalysis Society (AMMS), and Microscopy Australia.
