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Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning.

Dan Chen¹, Lin Bian², Hao-Yuan He¹

¹Department of Neurosurgery, The Third People's Hospital of Hefei, Hefei 230022, China.

Computational and Mathematical Methods in Medicine

|July 8, 2022

Summary

A deep learning algorithm using convolutional neural networks (CNN) accurately measures traumatic subdural hematoma (TSDH) volume. This automated method shows high consistency with manual segmentation, offering efficient and reliable TSDH volume assessment.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Medicine
Neurosurgery

Background:

Accurate traumatic subdural hematoma (TSDH) volume measurement is crucial for effective patient treatment.
Current methods for TSDH volume assessment may lack speed or precision.
Deep learning offers potential for automated and accurate medical image analysis.

Purpose of the Study:

To evaluate the consistency of TSDH volume measurements using a deep learning-based convolutional neural network (CNN) algorithm.
To compare the performance of CNN segmentation against manual segmentation and the ABC/2 formula.
To assess the efficiency and accuracy of CNN for TSDH volume calculation.

Main Methods:

A retrospective study analyzed 90 CT images from patients diagnosed with TSDH.
Hematoma volumes were measured using manual segmentation, CNN algorithm segmentation, and the ABC/2 formula.
Consistency was tested against manual segmentation as the gold standard; statistical analysis included percentage error and area under the curve (AUC).

Main Results:

The CNN algorithm demonstrated a lower percentage error compared to the ABC/2 formula.
No significant difference was found between CNN segmentation and manual segmentation (P > 0.05).
AUC values indicated strong performance for all methods, with manual segmentation (0.840) and CNN (0.832) closely comparable and outperforming ABC/2 (0.811).

Conclusions:

The CNN-based algorithm provides efficient and accurate segmentation for TSDH volume calculation.
This deep learning approach shows high consistency with manual segmentation, validating its clinical utility.
CNN offers a reliable tool for rapid and precise TSDH volume assessment in clinical practice.