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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Methodological considerations for evaluating deep learning segmentation models in digital pathology whole-slide

Arian Arab¹, Victor Garcia¹, Seyed Kahaki¹

¹United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|June 17, 2026

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This summary is machine-generated.

Deep learning (DL) models for whole-slide image (WSI) segmentation in breast cancer show improved performance with color normalization, especially when data sources differ. Careful consideration of analysis units is crucial for accurate performance evaluation.

Area of Science:

Computational pathology
Digital pathology
Artificial intelligence in medicine

Background:

Automated whole-slide image (WSI) analysis using deep learning (DL) facilitates disease detection, classification, segmentation, and prognosis.
Performance evaluation is critical for the success of big data technologies in pathology.

Purpose of the Study:

To evaluate DL segmentation models on a breast cancer WSI dataset.
To investigate methodological challenges in assessing WSI segmentation models.
To assess the impact of color normalization on model performance across different data sources.

Main Methods:

Evaluated DL model performance for segmenting tumoral and stromal regions in breast cancer WSIs.
Assessed the effect of color normalization on model performance with cross-source data.

Keywords:

Dice score aggregation digital pathology medical image segmentation performance evaluation uncertainty estimate whole-slide image

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Introduced three methods for aggregating segmentation performance (pixels, ROIs, slides).

Employed a bootstrap method for estimating slide-level performance variance.

Main Results:

Different units of analysis yielded varying mean performance estimates and uncertainty levels.
Color normalization significantly enhanced DL model performance when training and testing data originated from different sources.
The choice of aggregation unit impacts performance evaluation outcomes.

Conclusions:

The study highlights the critical role of image acquisition, study design, and statistical analysis in evaluating computational pathology applications.
Robust performance evaluation methodologies are essential for reliable DL model deployment in digital pathology.
Standardized evaluation metrics and methods are needed for consistent assessment of WSI analysis tools.