#google-summer-of-code

[openworm]

Google Summer of Code kick-off

The list of accepted GSoC particpants has been published and students have been notified of their status. Congratulations to Vinay Varma, Emily Thomas, and Jaideep Seth on being selected! We're glad to have you with us and look forward to collaborating with you throughout the summer.

On behalf of all OpenWorm contributors, I offer a sincere thanks to all those who submitted a proposal: your enthusiasm for OpenWorm and for Free and Open Source Software (FOSS) shone through beautifully. To those who were not accepted this year, especially if this is your first time contributing to FOSS, please continue your involvement in free software and in open science: there are so many problems out there, and they become so much more achievable when we work together.

[nvinayvarma189]

Coding Period (Week - 1)

May 25 - May 31

This marks the beginning of the coding period.

What Have I done:

1. Implemented the W-net deep learning model architecture. Started preparing the dataset by using image augmentation techniques. Worked on writing custom loss function (soft cut normalized loss) for training the encoder and reconstruction loss for training the whole autoencoder.

2. Discussed the ideas for phase 3 of the GSoC phase. We have considered the four ideas

2.1 Extending GSoC 2018 project to Geppetto. Requires a lot of engineering effort. So this is not recommended.

2.2 Create a visualization skin for GSoC 2018 project. Focus on networks and 3-D modeling. Significant time overhead for setting up the environment and learning about the technologies

2.3 Work with Morphozoic and CompuCell3D tool to make a visualization of an embryo of C. Elegans. Required Cpp experience and the work is more related to the visualization field.

2.4 Cell tracking and tagging from Brightfield embryogenesis movies. We have a video like this. We have to segment and track the cells as the process moves on.  This idea is related to the computer vision field and this is something I might end up working on for phase 3 of GSoC. 

3. Presented my research work during the community bonding period and explained my approach for the project to the OpenWorm community. You can find the presentation here.

What will I do next:

1. Continue working on the W-net paper implementation. Implement the post-processing techniques  “Conditional Random Field smoothening” and “Hierarchical Segmentation”.

2. Train the W-net model on the available SPIM images and test the results.

[nvinayvarma189]

Community Bonding (Week 2)

May 17 - May 24

Finally, I’m done with all my semester exams, academic projects, and reviews this week. That gave me a lot of relief. This post is to summarize all the work that I have done this week and my plans for next week.

What Have I done:

1. I started with Jython programming to see if I can do Image Processing with Python code right from the plugin. Basically, pass the image from the plugin code(Java) to Python, do the required processing and then return the processed image to the plugin for the user to see the results. 

I wanted Python because I could build deep learning models easily with the help of the vast Deep Learning framework ecosystem available for Python. However, that did not go well as I found out that Jython is a bit ineffective in this case. See this thread.

Some other potential options are Weka and DeepLearning4J. Weka is more of a plug and play tool. It is useful for beginners to quickly try out basic ML/DL models. If one wants to customize the model and play with the parameters, it is not recommended. Refer this to learn more about the pros and cons of Weka.

I have tried out DeepLearning4J and it looks awesome. I implemented a basic CNN in Java and the training speed is exceptional. The only problem I faced is that the tutorials on their channel are outdated. I plan to stick with DeepLearning4J for this project.

2. I’m supposed to give a slide show presentation to my mentor on May 31st. I have prepared content for that.

3. Participated in the weekly hackathon conducted 17th May.

What will I do next:

1. Derive a solid objective for building the feature analysis tool. I’m a bit unclear about what would be a good thing to do for feature analysis (during phase 3 of GSoC).

2. Present the project. Report my progress and explain my approach for the project.

Cheers!!

[nvinayvarma189]

Community Bonding (Week 1)

So, the results of GSoC’19 came out on May 6th, 2019 and I’m glad that I have been selected as a GSoC student with INCF. 

To know a bit about my project, please look at my previous post. 

What Have I done:

1. Have gone through the GSoC reading materials given by my mentor.

2. Setup the environment for developing plugins for ImageJ.

3. Downloaded this example plugin, made changes to the code, built the project, packaged it as a JAR file and finally added it as a plugin to ImageJ. I got myself familiar with the workflow.

4. Participated in two weekly hackathons (10th and 15th May). Have given an overview of my project to the OpenWorm community.

What will I do next:

1. Make use of ImageJ’s capability to add python scripts to plugins. Basically, try to manipulate an image with python code placed in the plugin.

2. See if I can add saved Deep Learning Models to ImageJ plugins with the help of Deeplearning4J.

3. Discuss the progress with my mentor and decide on what to do next.

[nvinayvarma189]

Introduction to the Project (16.2)

Semantic Image Processing for Developmental Data Science

I will be working for the Openworm foundation. Its primary goal is to understand the neural connections of C.Elegan, by creating a virtual replica of the worm. It is believed to be the first step that is to be taken to understand the rather complex connectomes like the human brain. 

Mentor: Bradly Alicea

The primary goal: To accurately segment the cells of C. Elegans from the SPIM images taken during its Embryogenesis process. Once I’m done with this I can focus on creating an automated feature analysis tool (TBD).

Previous Work: In GSoC’17, an ImageJ plugin has been built to segment the cells from SPIM images. Many classical image processing techniques were integrated into the plugin. We can achieve better accuracy by adding Deep Learning methods. However, the main problem is the lack of labeled data.

Solution Approach: Using Semi-Supervised and Unsupervised Deep Learning techniques for image segmentation.

Here is my accepted proposal. You can look into this for a detailed explanation of the whole project. 

I will be uploading all the code that I will write during this summer to this GitHub Repo.

In case you are having some questions or just want to connect, please feel free to contact me via my email.

Cheers!!