Description: Integration of Tumor Segmentation Model
This Pull Request introduces significant enhancements to our image processing pipeline by integrating a tumor segmentation model. The primary objective of these changes is to facilitate the segmentation of tumors from multiplexed images, thereby improving the accuracy and efficiency of our medical imaging analysis.
Major Updates:
Grayscale Conversion Function (rgb2gray): This function is designed to convert 3D RGB images into grayscale. The conversion is based on the linear mapping formula used by ImageMagick, Y = 0.299 * R + 0.587 * G + 0.114 * B. This transformation allows us to prepare our images for the subsequent segmentation process.
Tumor Segmentation Function (segment_tumors): This function leverages the capabilities of the LIONZ model to segment tumors from multiplexed images. The integration of this function marks a significant step forward in our ability to accurately identify and analyze tumor regions within our dataset.
These additions are expected to significantly enhance the functionality of our image processing pipeline, providing more precise and detailed analysis capabilities. We look forward to the improvements these changes will bring to our medical imaging analysis.
Description: Integration of Tumor Segmentation Model
This Pull Request introduces significant enhancements to our image processing pipeline by integrating a tumor segmentation model. The primary objective of these changes is to facilitate the segmentation of tumors from multiplexed images, thereby improving the accuracy and efficiency of our medical imaging analysis.
Major Updates:
Grayscale Conversion Function (
rgb2gray): This function is designed to convert 3D RGB images into grayscale. The conversion is based on the linear mapping formula used by ImageMagick,Y = 0.299 * R + 0.587 * G + 0.114 * B. This transformation allows us to prepare our images for the subsequent segmentation process.Tumor Segmentation Function (
segment_tumors): This function leverages the capabilities of theLIONZmodel to segment tumors from multiplexed images. The integration of this function marks a significant step forward in our ability to accurately identify and analyze tumor regions within our dataset.These additions are expected to significantly enhance the functionality of our image processing pipeline, providing more precise and detailed analysis capabilities. We look forward to the improvements these changes will bring to our medical imaging analysis.