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Related Experiment Video

Updated: Jun 25, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

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Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.

Alessia De Biase¹, Liv Ziegfeld², Nanna Maria Sijtsema³

¹Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands; Data Science Center in Health (DASH), University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands.

Computers in Biology and Medicine

|May 31, 2024

Summary

This summary is machine-generated.

Deep learning-generated tumor probability maps offer an intuitive and explainable alternative for head and neck cancer segmentation, improving radiation oncologist workflows. This method enhances tumor auto-segmentation model generalizability.

Keywords:

Adaptive segmentation Clinical explainability Deep learning Head and neck cancer Human-centered artificial intelligence Tumor segmentation

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Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

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Published on: January 11, 2020

Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Radiation Therapy Planning

Background:

Manual tumor segmentation in head and neck cancer is subjective due to variations in imaging.
Variability in manual contours limits the generalizability of deep learning (DL) auto-segmentation models.
A novel DL-based method was developed to output tumor probability maps instead of fixed contours.

Purpose of the Study:

To demonstrate the clinical relevance and intuitiveness of DL-generated probability maps for tumor segmentation.
To present a more suitable solution for radiation oncologists in gross tumor volume segmentation.
To improve the performance of DL-based tumor auto-segmentation models.

Main Methods:

Development of a DL-based method producing voxel-wise tumor probability maps.
Design of a graphical user interface (GUI) for interacting with probability maps.
Conducting a user study with nine radiation oncology experts assessing usability and functionality.

Main Results:

Radiation oncologists preferred a rainbow colormap for intuitive visualization of tumor probability maps.
A slider feature for threshold selection was appreciated for contour generation.
The prototype demonstrated excellent usability and positive integration into clinical workflows.

Conclusions:

DL-generated tumor probability maps are explainable, transparent, and intuitive.
These maps represent a superior alternative to single-output tumor segmentation models.
The approach enhances tumor auto-segmentation and assists radiation oncologists.