Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation

TL;DR

This paper introduces a segmentation-based framework with dynamic programming for accurate brain midline delineation in CT images, addressing challenges in severe cases where anatomical points are hard to detect.

Contribution

The study proposes a novel three-stage framework combining image alignment, a specialized detection network, and pathfinding for improved midline delineation.

Findings

Achieves superior performance on in-house and public datasets.

Effectively handles discontinuities in midline detection.

Outperforms existing anatomical point localization methods.

Abstract

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI). The automated midline delineation not only improves the assessment and clinical decision making for patients with stroke symptoms or head trauma but also reduces the time of diagnosis. Nevertheless, most of the previous methods model the midline by localizing the anatomical points, which are hard to detect or even missing in severe cases. In this paper, we formulate the brain midline delineation as a segmentation task and propose a three-stage framework. The proposed framework firstly aligns an input CT image into the standard space. Then, the aligned image is processed by a midline detection network (MD-Net) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map. Finally, we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation. Experimental results show that our proposed framework can achieve superior performance on one in-house dataset and one public dataset.