DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images

TL;DR

This paper introduces DMCIE, a diffusion model framework that improves brain tumor segmentation accuracy in MRI images by leveraging error maps and input concatenation, outperforming existing methods on the BraTS2020 dataset.

Contribution

The paper presents a novel diffusion-based segmentation approach that incorporates error maps and input concatenation to enhance accuracy in brain tumor MRI segmentation.

Findings

Achieved a Dice Score of 93.46 on BraTS2020

Outperformed several state-of-the-art diffusion methods

Enhanced segmentation focus on misclassified regions

Abstract

Accurate segmentation of brain tumors in MRI scans is essential for reliable clinical diagnosis and effective treatment planning. Recently, diffusion models have demonstrated remarkable effectiveness in image generation and segmentation tasks. This paper introduces a novel approach to corrective segmentation based on diffusion models. We propose DMCIE (Diffusion Model with Concatenation of Inputs and Errors), a novel framework for accurate brain tumor segmentation in multi-modal MRI scans. We employ a 3D U-Net to generate an initial segmentation mask, from which an error map is generated by identifying the differences between the prediction and the ground truth. The error map, concatenated with the original MRI images, are used to guide a diffusion model. Using multimodal MRI inputs (T1, T1ce, T2, FLAIR), DMCIE effectively enhances segmentation accuracy by focusing on misclassified regions, guided by the original inputs. Evaluated on the BraTS2020 dataset, DMCIE outperforms several state-of-the-art diffusion-based segmentation methods, achieving a Dice Score of 93.46 and an HD95 of 5.94 mm. These results highlight the effectiveness of error-guided diffusion in producing precise and reliable brain tumor segmentations.