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DeSeg: auto detector-based segmentation for brain metastases.

Hui Yu¹, Zhongzhou Zhang¹, Wenjun Xia¹

¹College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.

Physics in Medicine and Biology

|December 19, 2022

Summary

This summary is machine-generated.

A new DeSeg framework accurately detects small brain metastases (BMs) and precisely outlines large ones for radiosurgery. This method improves sensitivity and segmentation metrics, offering faster processing for clinical applications.

Keywords:

brain metastasis auto-delineation coarse-to-fine framework fine segmentation small object detection stereotactic radiosurgery

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Area of Science:

Medical Imaging
Radiosurgery
Artificial Intelligence in Medicine

Background:

Accurate delineation of brain metastases (BMs) is crucial for effective stereotactic radiosurgery.
Existing auto-delineation methods struggle with missing small lesions or inaccurate contours for large ones.
Clinical demand requires methods that are sensitive to small lesions and precise for larger ones.

Purpose of the Study:

To propose and evaluate a novel coarse-to-fine framework, DeSeg (detector-based segmentation), for improved BM auto-delineation.
To incorporate object-level detection into pixel-wise segmentation to meet clinical demands for both small and large lesions.
To assess the performance and computational efficiency of DeSeg for potential real-time processing.

Main Methods:

Developed DeSeg, a three-component framework including a center-point-guided single-shot detector, a multi-head U-Net segmentation model, and a data cascade unit.
Utilized 240 contrast-enhanced T1-weighted MRI scans from BM patients, randomly split into training, validation, and testing datasets.
Evaluated performance using object-based sensitivity and PPV for small lesions (≤1.5 cc) and DSC, ASSD, and HD95 for large lesions (>1.5 cc).

Main Results:

DeSeg achieved a sensitivity of 0.91 and PPV of 0.77 for small lesions.
For large lesions, DeSeg demonstrated a DSC of 0.86, ASSD of 0.76 mm, and HD95 of 2.31 mm.
The framework showed competitive segmentation performance with faster processing speeds compared to existing 3D models.

Conclusions:

DeSeg effectively addresses the clinical need for accurate brain metastasis delineation, excelling in both sensitivity for small lesions and contour accuracy for large ones.
The proposed method offers a promising solution for stereotactic radiosurgery planning, balancing performance with computational efficiency.
DeSeg represents a significant advancement in automated medical image segmentation for neuro-oncology applications.