The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSoC 2016 – 2019. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. ImageJ is a public domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins.
The project idea: The existing machine learning model of Active Segmentation is based on the Weka library. However, this is limited to traditional machine learning approaches. The objective of the project will be to incorporate deep-learning functionality into the platform. Deep neural nets are capable of record-breaking accuracy.
The project is a continuation of GSOC 2020. The project will start from an already available codebase implemented using Deeplearning4j. At present, there are 2 implemented architectures U-Net — an architecture for biomedical image segmentation, and SegNet — a deep learning semantic segmentation architecture. The candidate will work out the GUI integration with the rest of the ASP/IJ platform.
- Fix existing issues and bugs
- Get familiar with Deeplearning4j
- GUI implementation and integration of the Deeplearning4j functionality
Minimal set of deliverables:
- Requirement specification – Prepared by the candidate after understanding the functionality.
- System Design – Detailed plan for the development of the plugin and test cases.
- Implementation and testing – Details of implementation and testing of the platform.
Desired skills: Java, Machine Learning
Mentors: Sumit Vohra, ZIB, Berlin, Germany; Dimiter Prodanov @dprodanov, INCF Belgian Node (backup)
ImageJ: https://imagej.nih.gov/ 6
Weka https://www.cs.waikato.ac.nz/ml/weka/ 3
Active Segmentation : https://github.com/sumit3203/ACTIVESEGMENTATION 6
Deeplearning4J: https://deeplearning4j.org/ 1
Tags: ImageJ, segmentation, machine learning, deep learning, GUI