Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026
Accuracy assessment of a hybrid deep learning and image processing approach for lung CT segmentation in external radiotherapy.
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Sekkat Hamza, Khallouqi Abdellah, Berrada Yasmina, Talbi Mohammed, Bannan Ahmed, Mouden Oussama El, Halimi Abdellah, Madkouri Youssef, Rhazouani Omar El
Аннотация
BACKGROUND: Accurate lung segmentation in computed tomography (CT) is a fundamental step for numerous clinical applications, including radiotherapy planning and thoracic disease diagnosis. Manual segmentation, however, remains time-consuming and operator-dependent. This study aims to develop a fully automated segmentation framework combining a well-structured pipeline of image processing techniques with a deep learning model based on a U-Net architecture and a pre-trained VGG16 encoder.
MATERIALS AND METHODS: Ninety anonymized thoracic CT scans, including lung and breast cancer patients, were collected, pre-processed and used to generate binary lung masks using a custom Python pipeline. The dataset was split patient-wise into training (70%), validation (15%) and testing (15%) subsets. The model was trained using binary crossentropy loss, the Adam optimizer and data augmentation strategies to improve generalization.
RESULTS: High correlations were observed between automatically computed lung volumes and mean Hounsfield units (HU) compared to the treatment planning system (TPS) references (R2 = 0.84 for volume, R2 = 0.72 for HU, both with p < 1 × 10-33) and the deep learning model achieved a mean Dice similarity coefficient (DSC) of ~95% and intersection over union (IoU) of ~95% on an independent test set. Mean loss and pixel-wise accuracy were 0.004 and ~95%, respectively.
CONCLUSIONS: The proposed VGG16-based U-Net model demonstrates high accuracy and robustness in segmenting lungs from thoracic CT scans, achieving ~95% DSC and ~99% pixel-wise accuracy. It effectively replaces manual segmentation with a fully automated, scalable solution. This framework streamlines lung segmentation in clinical radiotherapy workflows, reducing operator variability and time consumption.
MATERIALS AND METHODS: Ninety anonymized thoracic CT scans, including lung and breast cancer patients, were collected, pre-processed and used to generate binary lung masks using a custom Python pipeline. The dataset was split patient-wise into training (70%), validation (15%) and testing (15%) subsets. The model was trained using binary crossentropy loss, the Adam optimizer and data augmentation strategies to improve generalization.
RESULTS: High correlations were observed between automatically computed lung volumes and mean Hounsfield units (HU) compared to the treatment planning system (TPS) references (R2 = 0.84 for volume, R2 = 0.72 for HU, both with p < 1 × 10-33) and the deep learning model achieved a mean Dice similarity coefficient (DSC) of ~95% and intersection over union (IoU) of ~95% on an independent test set. Mean loss and pixel-wise accuracy were 0.004 and ~95%, respectively.
CONCLUSIONS: The proposed VGG16-based U-Net model demonstrates high accuracy and robustness in segmenting lungs from thoracic CT scans, achieving ~95% DSC and ~99% pixel-wise accuracy. It effectively replaces manual segmentation with a fully automated, scalable solution. This framework streamlines lung segmentation in clinical radiotherapy workflows, reducing operator variability and time consumption.