Post-Heuristic Cancer Segmentation Refinement over MRI Images and Deep Learning Models

Abstract

Lately, deep learning methods have greatly improved the accuracy of brain-tumor segmentation, yet slice-wise inconsistencies still limit reliable use in clinical practice. While volume-aware 3D convolutional networks achieve high accuracy, their memory footprint and inference time may limit clinical adoption. This study proposes a resource-conscious pipeline for lower-grade-glioma delineation in axial FLAIR MRI that combines a 2D Attention U-Net with a guided post-processing refinement step. Two segmentation backbones, a vanilla U-Net and an Attention U-Net, are trained on 110 TCGA-LGG axial FLAIR patient volumes under various loss functions and activation functions. The Attention U-Net, optimized with Dice loss, delivers the strongest baseline, achieving a mean Intersection-over-Union (mIoU) of 0.857. To mitigate slice-wise inconsistencies inherent to 2D models, a White-Area Overlap (WAO) voting mechanism quantifies the tumor footprint shared by neighboring slices. The WAO curve is smoothed with a Gaussian filter to locate its peak, after which a percentile-based heuristic selectively relabels the most ambiguous softmax pixels. Cohort-level analysis shows that removing merely 0.1–0.3% of ambiguous low-confidence pixels lifts the post-processing mIoU above the baseline while improving segmentation for two-thirds of patients. The proposed refinement strategy holds great potential for further improvement, offering a practical route for integrating deep learning segmentation into routine clinical workflows with minimal computational overhead.