Description

The current process for generating the rotation MIP of fused PET images and the corresponding tumor segmentation masks is excessively slow. This issue aims to address and enhance the performance of this crucial step in our imaging pipeline.

Objectives

• Performance Optimization: Identify and eliminate bottlenecks in the current rotation MIP and mask generation process.
• Algorithm Efficiency: Explore more efficient algorithms or libraries that can accelerate the generation of rotation MIPs and tumor masks.
• User Experience: Improve the user experience by reducing the waiting time for these processes to complete.

Expected Impact

• Processing Speed: Significantly reduce the time required to generate rotation MIPs and tumor masks without compromising on quality.
• Scalability: Enhance the tool's ability to handle larger datasets.

Description

We are planning to transition our current medical image segmentation tool from using nnU-Net as its core segmentation framework to Auto3DSeg. This change aims to leverage Auto3DSeg's automated algorithm selection, data processing, hyperparameter tuning, and benchmarking capabilities, which could potentially enhance our tool's performance and user experience.

Objectives

• Algorithm Flexibility: Integrate Auto3DSeg to allow dynamic selection of the best segmentation algorithms based on the dataset characteristics.
• Data Processing Automation: Utilize Auto3DSeg's data processing pipelines to streamline our preprocessing and postprocessing steps.
• Hyperparameter Optimization: Implement Auto3DSeg's automated hyperparameter tuning to improve segmentation accuracy without manual intervention.
• Benchmarking: Establish a benchmarking system within our tool to evaluate different models and configurations against our datasets.
• Customization and Extensibility: Ensure that the integration of Auto3DSeg does not hinder the customization and extensibility of our tool.

Expected Impact

• Performance: Aim for improved segmentation accuracy and processing speed.
• Usability: Reduce the need for manual configurations, making the tool more accessible to non-expert users.
• Research and Development: Facilitate ongoing research by allowing easy comparison and testing of new segmentation algorithms.

Implementation Plan

1. Assessment: Evaluate the current nnU-Net implementation in our tool and identify the components that need replacement.
2. Integration: Develop a plan for the phased integration of Auto3DSeg, including necessary refactoring of existing code.
3. Testing: Define a testing protocol to ensure that the new integration maintains or improves the current tool's performance.
4. Documentation: Update the documentation to reflect changes in the tool's operation and usage following the integration of Auto3DSeg.
5. Release: Plan a beta release of the tool with the new Auto3DSeg core to gather user feedback and performance data.

Potential Challenges

• Compatibility issues between Auto3DSeg and our current data formats and workflows.
• Ensuring that the Auto3DSeg integration is as robust and reliable as our current nnU-Net implementation.

Request for Comments

We invite all contributors and users to discuss the proposed changes. Any insights into the following areas would be particularly appreciated:

• Experiences with Auto3DSeg and its integration into existing projects.
• Potential pitfalls or considerations in transitioning from nnU-Net to Auto3DSeg.
• Suggestions for testing and validating the new setup.

Timeline

We aim to start the integration process by [insert start date], with a tentative timeline of [insert weeks/months] for completion. Progress updates will be posted in this issue thread.

Objective

To test the generalizability of our tumor segmentation algorithm across different cancer types, particularly breast cancer, using synthetic PET datasets derived from our current training data.

Background

Our current training dataset consists of lymphoma, mesothelioma, and lung cancer PET images. We aim to investigate whether the segmentation model can generalize its learnings to identify tumors in breast cancer, which is not part of the training set.

Methodology

• Develop a method to create synthetic breast cancer PET images by altering existing tumor regions within our dataset.
• Ensure that the synthesized images are varied enough to represent the heterogeneity of breast cancer presentations.
• Maintain realism in the synthetic datasets to closely mimic actual breast cancer PET images.

Evaluation Metrics

• Segmentation accuracy on real Breast cancer images.
• Analysis of false positives and false negatives to assess the model's specificity and sensitivity.

Implementation Steps

1. Algorithm Development: Create an algorithm for artificial tumor manipulation within PET images to generate the synthetic datasets.
2. Validation: Ensure that the synthetic images are vetted by domain experts for realism and relevance.
3. Testing: Run the trained model on the synthetic datasets and collect performance data.
4. Analysis: Compare results with the model's performance on the original training datasets and document findings.

Challenges

• Maintaining a balance between artificial manipulation and realistic presentation of tumors.
• Determining the extent to which the synthetic images can represent real breast cancer cases.