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Hybrid µCT-FMT imaging and image analysis

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Transfer Learning with Limited Data for Mice Lung Segmentation in Synchrotron X-Ray Tomography.

Andjela Blagojević^1,2, Ognjen Obradović³, Tijana Geroski^3,4

¹Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia. andjela.blagojevic@kg.ac.rs.

Annals of Biomedical Engineering

|March 3, 2026

Summary

This study introduces a novel transfer learning method for segmenting mouse lungs in synchrotron radiation-based X-ray tomographic microscopy (SRXTM) images. The approach significantly reduces the need for extensive data annotation, achieving accurate lung segmentation with limited labeled images.

Keywords:

Lung segmentation Synchrotron radiation-based X-ray tomographic microscopy imaging Transfer learning with limited data U-net

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Computational Biology
Biomedical Engineering

Background:

Accurate segmentation of lung structures is crucial for understanding lung development and disease.
Traditional segmentation methods require large annotated datasets, which are difficult and costly to obtain, especially for complex small-scale biological samples like mouse lungs.
Annotating mouse lungs from synchrotron radiation-based X-ray tomographic microscopy (SRXTM) images is particularly challenging due to their small size, complexity, and subtle boundaries.

Purpose of the Study:

To develop a novel approach for segmenting mouse lungs in SRXTM images using transfer learning with limited annotated data.
To address the challenges of unclear tissue-air demarcation and the scarcity of annotated datasets in medical imaging.
To enable more efficient and accurate analysis of lung development, structural changes, and disease.

Main Methods:

Utilized a U-net model pretrained on lung section images.
Applied transfer learning to fine-tune the model for segmenting entire mouse lungs in SRXTM images.
Evaluated the model's performance using a small dataset of 5 labeled entire lung image/mask pairs for training and 2 pairs for testing.

Main Results:

The U-net model achieved high accuracy in segmenting lung sections, with a Dice Similarity Coefficient (DSC) of 0.9069 and a mean Intersection over Union (mIOU) of 0.895.
After transfer learning for entire lung segmentation, the model maintained competitive performance with DSC = 0.8915 and mIOU = 0.8895.
The method demonstrated effectiveness even with a minimal number of annotated images (5 pairs).

Conclusions:

The proposed transfer learning approach is effective for segmenting mouse lungs in SRXTM images.
This method significantly reduces the requirement for annotated data compared to traditional techniques.
The findings highlight the potential of transfer learning to overcome data limitations in medical image analysis.