MRI and histology co-registration algorithm

MATLAB was used to convert the full resolution binary masks of PIMO-positive pixels generated in VisioPharm software (.mld files) into .mat files, which was then co-registered with MRI slices and used as input to the build DL models to identify hypoxic habitats.

Information about positive pixel areas in .mld files is stored as a collection of polygons. Each polygon is defined by a list of consecutive vertices, which are connected by lines. The order of the polygons defines which one encompasses a positive region and which one is a boundary of a negative one (e.g. hole). In order to transform that information into an image of given resolution and store it as a .mat file in MATLAB, each polygon was drawn into an image matrix using Bresenham's line algorithm and then filled accordingly using queue-scanline algorithm going from top of the image to bottom.

The PIMO-positive mask for each histology slice was co-registered with the mp-maps of the corresponding MRI slice, according to a method previously described in ³⁰. Briefly, custom written MATLAB code was used to perform affine 2D registration based on manual detection of 4 corresponding landmarks in histology and MRI images. Prior to co-registration, slices where the tissues were broken or with missing parts were excluded from co-registration analyses.

Dice similarity coefficients (DSC) were calculated between each MRI slice and its corresponding histology slice by creating binary masks for both slices and measuring the similarity between the masks using Dice formula in equation 1. The similarity score ranges between 0 and 1. A score close or equal to 1 indicates the slices are very similar or identical. More specifically, if M is the binary mask of the MRI slice and H is the histology binary mask, the DSC score is obtained by the following Dice equation: