Lung and Lung Tumor Segmentation of CT Images During MWA Therapy Using AI Algorithm

N. Mahmoodian, Harshita Thadesar, Maryam Sadeghi, Marilena Georgiades, Maciej Pech, Christoph Hoeschen


Microwave ablation (MWA) therapy as a thermal ablation procedure is an excellent alternative to open surgery for tumor treatment. The technique is considered advantageous for patients who are not candidates for open surgery due to factors such as age, anatomic limitations, resection, etc. Computed tomography (CT) is a commonly used interventional imaging modality during MWA therapy for localizing the tumor and finalizing the tumor treatment process. However, the CT scan of the body usually includes neighboring organs that are not relevant to lung tumor MWA therapy. Therefore, the segmentation of the lung and lung tumor in CT images provides valuable information about the tumor margin. This information can assist physicians in precisely and completely destroying the tumor during the MWA procedure. To solve the aforementioned problem, deep learning (DL), in particular, achieves a higher level of accuracy in segmentation than machine learning techniques due to its composition of multiple learning layers. The immediate goal is to distinguish among the different tissue structures of the tumor, healthy tissue, and the ablated area in lung CT images using the DL method to segment the organ and cancer area. Researchers have proposed various segmentation models. However, different segmentation tasks require different perception fields. In this study, we propose a new DL model that includes a residual block based on the U-Net model to accurately segment the lung organ and lung tumor tissue. The dataset consists of lung CT images acquired during MWA therapy using a CT scanner at the University Hospital Magdeburg. Manual tumor segmentation has been performed and confirmed by physicians. The results of our proposed method can be compared with those of the U-net model with a SSIM of 90%. Furthermore, accurately determining the margin area of the tumor tissue can decrease insufficient tumor ablation, which often leads to tumor recurrence. We anticipate that our proposed model can be generalized to perform tumor segmentation on CT images of different organs during MWA treatment. Finally, we hope that this method can achieve sufficient accuracy to decrease tumor recurrence and enable dose reduction for patients in interventional CT imaging.


Doi: 10.28991/SciMedJ-2023-05-01-01

Full Text: PDF


Deep Learning (DL); Artificial Intelligent (AI); Lung Tumor Segmentation; Microwave Ablation (MWA) Therapy.


Ait Skourt, B., El Hassani, A., & Majda, A. (2018). Lung CT image segmentation using deep neural networks. Procedia Computer Science, 127, 109–113. doi:10.1016/j.procs.2018.01.104.

Mona O. Aboelezz, M.D., E. H. A. E. M. S. ., Sameh S., H. E. D. M., & Vogl, M.D., T. J. (2020). Role of Microwave Ablation in Treatment of Lung Tumors. The Medical Journal of Cairo University, 88(6), 1117–1129. doi:10.21608/mjcu.2020.110849.

Gomez, A. M. L., Santana, P. C., & Mourão, A. P. (2020). Dosimetry study in head and neck of anthropomorphic phantoms in computed tomography scans. SciMedicine Journal, 2(1), 38-43. doi:10.28991/SciMedJ-2020-0201-6.

Farheen, F., Shamil, M. S., Ibtehaz, N., & Rahman, M. S. (2021). Segmentation of Lung Tumor from CT Images using Deep Supervision. arXiv preprint arXiv:2111.09262.

Farheen, F., Shamil, M. S., Ibtehaz, N., & Rahman, M. S. (2022). Revisiting segmentation of lung tumors from CT images. Computers in Biology and Medicine, 144, 105385. doi:10.1016/j.compbiomed.2022.105385.

Chlebus, G., Schenk, A., Moltz, J. H., van Ginneken, B., Hahn, H. K., & Meine, H. (2018). Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific Reports, 8(1). doi:10.1038/s41598-018-33860-7.

Mahmoodian, N., Thadesar, H., Sadeghi, M., Georgiades, M., Pech, M., & Hoeschen, C. (2022). Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net. Current Directions in Biomedical Engineering, 8(2), 49–52. doi:10.1515/cdbme-2022-1014.

Dutande, P., Baid, U., & Talbar, S. (2022). Deep Residual Separable Convolutional Neural Network for lung tumor segmentation. Computers in Biology and Medicine, 141. doi:10.1016/j.compbiomed.2021.105161.

Hu, H., Li, Q., Zhao, Y., & Zhang, Y. (2021). Parallel Deep Learning Algorithms with Hybrid Attention Mechanism for Image Segmentation of Lung Tumors. IEEE Transactions on Industrial Informatics, 17(4), 2880–2889. doi:10.1109/TII.2020.3022912.

He, B., Hu, W., Zhang, K., Yuan, S., Han, X., Su, C., Zhao, J., Wang, G., Wang, G., & Zhang, L. (2022). Image segmentation algorithm of lung cancer based on neural network model. Expert Systems, 39(3). doi:10.1111/exsy.12822.

Skourt, B. A., Nikolov, N. S., & Majda, A. (2019). Feature-extraction methods for lung-nodule detection: A comparative deep learning study. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), IEEE, December 2019, 1-6.

Rahman, T., Khandakar, A., Kadir, M. A., Islam, K. R., Islam, K. F., Mazhar, R., Hamid, T., Islam, M. T., Kashem, S., Mahbub, Z. Bin, Ayari, M. A., & Chowdhury, M. E. H. (2020). Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access, 8, 191586–191601. doi:10.1109/ACCESS.2020.3031384.

Pang, T., Guo, S., Zhang, X., & Zhao, L. (2019). Automatic lung segmentation based on texture and deep features of HRCT images with interstitial lung disease. BioMed Research International, 2019. doi:10.1155/2019/2045432.

Zhao, C., Xu, Y., He, Z., Tang, J., Zhang, Y., Han, J., Shi, Y., & Zhou, W. (2021). Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recognition, 119, 108071. doi:10.1016/j.patcog.2021.108071.

Monshi, M. M. A., Poon, J., Chung, V., & Monshi, F. M. (2021). CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR. Computers in Biology and Medicine, 133, 104375. doi:10.1016/j.compbiomed.2021.104375.

Diniz, J. O. B., Quintanilha, D. B. P., Santos Neto, A. C., da Silva, G. L. F., Ferreira, J. L., Netto, S. M. B., Araújo, J. D. L., Da Cruz, L. B., Silva, T. F. B., Caio, C. M., Ferreira, M. M., Rego, V. G., Boaro, J. M. C., Cipriano, C. L. S., Silva, A. C., de Paiva, A. C., Junior, G. B., de Almeida, J. D. S., Nunes, R. A., … Gattass, M. (2021). Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning. Multimedia Tools and Applications, 80(19), 29367–29399. doi:10.1007/s11042-021-11153-y.

Cao, F., & Zhao, H. (2021). Automatic lung segmentation algorithm on chest x-ray images based on fusion variational auto-encoder and three-terminal attention mechanism. Symmetry, 13(5), 814. doi:10.3390/sym13050814.

Full Text: PDF

DOI: 10.28991/SciMedJ-2023-05-01-01


  • There are currently no refbacks.

Copyright (c) 2023 Naghmeh Mahmoodian