The Allen Cell Structure Segmenter is an open source toolkit developed at the Allen Institute for Cell Science for 3D segmentation of intracellular structures in fluorescence microscope images, which brings together classic image segmentation and iterative deep learning workflows. Details including algorithms, validations, and examples can be found in our bioRxiv paper or allencell.org/segmenter. This tutorial will focus on how to run the Allen Cell Structure Segmenter (both classic image segmentation workflow and iterative DL workflow) to get an accurate segmentation.
The Allen Cell Structure Segmenter is implemented as two packages: aicssegmentation (classic image segmentation) and aicsmlsegment (deep learning segmentation). The execution is based on three building blocks: Binarizer, Curator and Trainer. We will explain how each building block works and demonstrate with real examples.
Note: The image reader used in our package supports images in common formats, such as .tiff, .tif, .ome.tif. The only vendor specific format supported by the reader is .czi (the file format for ZEISS microscope). For other formats, images have to be converted to .tiff or .ome.tif in advance.
aicssegmentation(classic image segmentation): Installation instruction (available on Linux, MacOS, Windows)aicsmlsegment(deep learning segmentation): Installation instruction (requires NVIDIA GPU and Linux OS)
- Binarizer: documentation
- Curator: documentation
- Trainer: documentation
Deep learning (DL) is a very powerful approach for 3D image segmentation. But it is not as simple as collecting a set of segmentation ground truth, feeding them into a DL model and getting a perfect segmentation model. DL for 3D image segmentation is still being investigated in the field of computer vision (see top conferences organized by MICCAI and CVF ). It is possible to have a model trained with our package still failing to produce accurate result. This could be due to many reasons and finding out ways to improve the model is beyond the scope of this tutorial. Here, we want to focus on demonstrating how to use our package, the DL part of which is designed to (1) get a good segmentation model that could work on images existing wide variability (2) be flexible enough for advanced users to develop their own research on DL-based 3D segmentation.
The above flowchart is a simplified version of the segmenter showing the most important parts of the workflows. Binarizer can be either class segmentation algorithms or a DL model to compute the binary segmentation. Curator and Trainer are used to improve the segmentation from Binarizer when necessary. More details can be found in bioRxiv paper). Here, we will have demonstrations on two examples: first one only using Binarizer to solve the problem (i.e., a classic image segmentation workflow) and the other example also requiring Curator and Trainer (makes up the iterative DL workflow).
Link to the demo documentation
