SegNet-Based Framework for Robust Lung Tumor Segmentation from PET/CT Scans

Yessine  Amri; Amine Ben  Slama; Zouhair  Mbarki

doi:10.6180/jase.202607_30.008

SegNet-Based Framework for Robust Lung Tumor Segmentation from PET/CT Scans

Yessine Amri¹This email address is being protected from spambots. You need JavaScript enabled to view it., Amine Ben Slama², and Zouhair Mbarki³

¹Biochemistry Laboratory, Bechir Hamza Children’s Hospital, Tunis, 1007, Tunisia

²Research Laboratory of Biophysics and Medical Technologies LRBTM (LR13ES07), Higher Institute of Medical Technologies of Tunis (ISTMT), University of Tunis El Manar, Tunis,1006, Tunisia

³RIFTSI Research Laboratory, ENSIT, University of Tunis, Tunis, 1008, Tunisia

Received: July 25, 2025
Accepted: October 12, 2025
Publication Date: December 11, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202607_30.008

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, often due to late stage diagnosis and the complexity of tumor localization in thoracic imaging. Accurate and automated segmentation of lung tumors from PET/CT images is essential for early diagnosis, treatment planning, and outcome monitoring. Manual segmentation is time-consuming and prone to observer variability, underscoring the need for reliable deep learning-based solutions. This study proposes an automated lung tumor segmentation framework using the SegNet architecture, a deep encoder-decoder convolutional neural network. A dataset of 1,425PET/CT images, manually annotated by expert radiologists, was utilized. Data augmentation techniques were applied to improve generalization. SegNet was trained to perform pixel-wise binary classification, and its performance was benchmarked against the widely used U-Net model. Evaluation metrics included Accuracy, Recall, Dice coefficient, and Intersection over Union (IoU).The proposed SegNet model achieved strong segmentation performance across independent experiments. Average results were: Accuracy of 92.24%±1.42, Recall of 94.02% ±1.287, Dice coefficient of 93.47%±1.4, and IoU of 93.03%±1.2. Compared to U-Net (Dice: 92.18% ±1.081, IoU: 91.70%±1.287 ), SegNet demonstrated improved spatial boundary accuracy, particularly for tumors located near complex anatomical structures. Statistical tests confirmed the significance of the performance difference ( p < 0.05 ). The SegNet-based model provides accurate and robust segmentation of lung tumors in PET/CT images, outperforming U-Net under the same conditions. Its use of max-pooling indices enhances spatial precision, making it well-suited for clinical applications. Future work will explore 3Dextensions, multi-class segmentation, and multi-center validation to enhance its applicability in real-world diagnostic workflows.

Keywords: Lung tumor, PET/CT, Deep learning, SegNet, Medical image segmentation, U-Net

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