Learning Sample-Adaptive Intensity Lookup Table for Brain Tumor Segmentation

Abstract

Intensity variation among MR images increases the difficulty of training a segmentation model and generalizing it to unseen MR images. To solve this problem, we propose to learn a sample-adaptive intensity lookup table (LuT) that adjusts each image’s contrast dynamically so that the resulting images could better serve the subsequent segmentation task. Specifically, our proposed deep SA-LuT-Net consists of an LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific piece-wise linear intensity mapping function under the guide of the performance of the segmentation module. We develop our SA-LuT-Nets based on two backbone networks, DMFNet and the modified 3D Unet, respectively, and validate them on BRATS2018 dataset for brain tumor segmentation. Our experiment results clearly show the effectiveness of SA-LuT-Net in the scenarios of both single and multi-modalities, which is superior over the two baselines and many other relevant state-of-the-art segmentation models.