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

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Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net.

Shima Nofallah¹, Mojgan Mokhtari², Wenjun Wu³

¹University of Washington, Seattle, WA, 98195, USA. shimz@uw.edu.

Journal of Digital Imaging

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep learning pipeline for segmenting skin biopsies, improving melanoma diagnosis accuracy. The method uses minimal annotations, reducing expert workload and enhancing diagnostic capabilities for melanocytic lesions.

Keywords:

Dermatology Invasive melanoma Semantic segmentation Skin biopsy Sparse annotation Whole slide imaging

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Last Updated: Sep 24, 2025

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

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Melanoma diagnoses have surged, increasing the need for accurate diagnostic tools for melanocytic lesions.
Histologic evaluation of skin biopsies is critical for diagnosing melanoma and its precursors.
Current diagnostic methods require expert annotation, which is labor-intensive and time-consuming.

Purpose of the Study:

To develop an efficient deep learning pipeline for semantic segmentation of skin biopsies.
To reduce the annotation burden in training segmentation models for medical images.
To improve the accuracy and efficiency of diagnosing melanocytic lesions.

Main Methods:

A two-stage segmentation pipeline was developed.
The pipeline utilizes coarse and sparse annotations on small regions of whole slide images for training.
Deep learning image analysis was employed for semantic segmentation.

Main Results:

The proposed pipeline demonstrated promising performance in segmenting whole slide images.
The use of minimal annotations significantly reduced the annotation effort.
The method shows potential for complementing current diagnostic capabilities.

Conclusions:

The developed two-stage segmentation pipeline offers an effective solution for analyzing skin biopsies.
This approach can significantly decrease the workload associated with expert annotation.
The findings suggest a potential improvement in the diagnostic accuracy and efficiency of melanoma detection.