Patent ID: 11961618
Assignee: CITY UNIVERSITY OF HONG KONG
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. A method for training a task-interaction network (TI-Net) to jointly perform a segmentation task to locate prostate cancer areas and a classification task to assess aggressiveness of lesions based on a multi parametric-magnetic resonance imaging (mp-MRI) scan image in terms of a plurality of defined categories, the method comprising:
preparing a training dataset of mp-MRI images, each mp-MRI image having a labelled category and including a first type MRI slice and a second type MRI slice;
for each mp-MRI image:
registering the first type MRI slice with the second type MRI slice via non-rigid registration based on mutual information of the first and second type MRI slices;
normalizing intensity of the registered first and second type MRI slices with zero mean and unit variance;
center-cropping and resizing the normalized first and second type MRI slices in an axial plane to reduce noisy from irrelevant information;
spatially aligning and concatenating the cropped and resized first and second type MRI slices;
feeding the aligned and concatenated first and second type MRI slices to the TI-Net;
training a backbone network in the TI-Net to extract an initial discriminative feature representation from the aligned and concatenated first and second type MRI slices;
training an auxiliary segmentation branch in the TI-Net to generate an initial probability map and predict an initial lesion mask based on the initial discriminative feature representation;
training a classification branch in the TI-Net to determine a plurality of category prototypes corresponding to the plurality of defined categories respectively and predict a lesion aggressiveness based on the initial discriminative feature representation, the plurality of category prototypes and the initial lesion mask; and
training a main segmentation branch in the TI-Net to predict a lesion location based on the initial discriminative feature representation, the plurality of category prototypes and the predicted lesion aggressiveness.