DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation

TL;DR

This paper introduces DASN, a data-aware skilled network that enhances MR brain tissue segmentation accuracy by intelligently selecting models suited for specific data sets, validated on IBSR 18.

Contribution

The paper proposes a novel data-aware model selection approach that improves segmentation accuracy without additional training or data, based on dataset characteristics.

Findings

Achieved an average dice ratio of 88.06% on IBSR 18.

Outperformed individual models with dice ratios of 85.82% and 86.92%.

Validated effectiveness of data-aware model selection in medical image segmentation.

Abstract

Accurate segmentation of MR brain tissue is a crucial step for diagnosis, surgical planning, and treatment of brain abnormalities. Automatic and reliable segmenta-tion methods are required to assist doctor. Over the last few years, deep learning especially deep convolutional neural networks (CNNs) have emerged as one of the most prominent approaches for image recognition problems in various do-mains. But the improvement of deep networks always needs inspiration, which is rare for the ordinary. Until now,there have been reasonable MR brain tissue segmentation methods,all of which can achieve promising performance. These different methods have their own characteristic and are distinctive for data sets. In other words, different models performance vary widely on the same data sets and each model has what it is skilled in. It is on the basis of this, we propose a judgement to distinguish data sets that different models are good at. With our method, the segmentation accuracy can be improved easily based on the existing models, neither without increasing training data nor improving the network. We validate our method on the widely used IBSR 18 dataset and obtain average dice ratio of 88.06%,while it is 85.82% and 86.92% when only using separate one model respectively.