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Related Experiment Video

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

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

Published on: April 13, 2013

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model.

A J A J van de Schoot¹, G Schooneveldt¹, S Wognum¹

¹Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.

Medical Physics

|March 6, 2014

Summary

This summary is machine-generated.

This study presents an accurate, generic automatic bladder segmentation method for cone beam computed tomography (CBCT) using pretreatment data. The approach is independent of patient gender and position, improving segmentation accuracy with shape model expansion.

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Area of Science:

Medical Imaging
Radiotherapy Planning
Computational Anatomy

Background:

Accurate bladder segmentation is crucial for radiotherapy planning in pelvic tumors.
Existing segmentation methods often lack generalizability across different patient demographics and treatment positions.
Cone beam computed tomography (CBCT) is widely used for image-guided radiotherapy, necessitating robust auto-segmentation tools.

Purpose of the Study:

To develop and validate a generic, automated method for bladder segmentation on CBCT.
To ensure the method is independent of patient gender and treatment position (prone/supine).
To utilize only pretreatment imaging data for segmentation guidance.

Main Methods:

A patient-specific bladder shape model was generated from full and empty bladder contours on pretreatment CT scans.
The model guided iterative segmentation on CBCT by maximizing cross-correlation of grey value gradients.
Automatic adaptations for CBCT artifacts and manual local adaptations were implemented; shape model was expanded iteratively.

Main Results:

The method achieved high accuracy, with mean Dice Similarity Coefficients (DSC) ranging from 0.85 to 0.89 across different groups.
Surface distance errors (SDE) and standard deviations of contour-to-contour distances were low, indicating precise boundary delineation.
Manual adaptations and shape model expansion significantly improved segmentation performance (p < 0.01).

Conclusions:

The developed patient-specific shape model-based automatic bladder segmentation on CBCT is accurate and generic.
The method requires only two pretreatment imaging datasets and is independent of patient gender and treatment position.
The approach offers the flexibility of local manual segmentation adaptations for enhanced precision.