SCC-NET: segmentation of clinical cancer image for head and neck squamous cell carcinoma

Chien-Yu Huang; Cheng-Che Tsai; Lisa Alice Hwang; Bor-Hwang Kang; Yaoh-Shiang Lin; Hsing-Hao Su; Guan‐Ting Shen; Jun-Wei Hsieh

doi:10.1117/1.JMI.11.6.065501

21 November 2024 SCC-NET: segmentation of clinical cancer image for head and neck squamous cell carcinoma

Chien-Yu Huang, Cheng-Che Tsai, Lisa Alice Hwang, Bor-Hwang Kang, Yaoh-Shiang Lin, Hsing-Hao Su, Guan‐Ting Shen, Jun-Wei Hsieh

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 6, 065501 (November 2024). https://doi.org/10.1117/1.JMI.11.6.065501

Abstract

Purpose

Squamous cell carcinoma (SCC) accounts for 90% of head and neck cancer. The majority of cases can be diagnosed and even treated with endoscopic examination and surgery. Deep learning models have been adopted for various medical endoscopy exams. However, few reports have been on deep learning algorithms for segmenting head and neck SCC.

Approach

Head and neck SCC pre-treatment endoscopic images during 2016–2020 were collected from the Kaohsiung Veterans General Hospital Department of Otolaryngology-Head and Neck Surgery. We present a new modification of the neural architecture search-U-Net-based model called SCC-Net for segmenting our enrolled endoscopic photos. The modification included a new technique called “Learnable Discrete Wavelet Pooling” to design a new formulation that combines the outputs of different layers using a channel attention module and assigns weights based on their importance in the information flow. We also incorporated the cross-stage-partial design from CSPnet. The performance was compared with other eight state-of-the-art image segmentation models.

Results

We collected a total of 556 pathologically confirmed SCC photos. The new SCC-Net algorithm achieves a high mean intersection over union (mIOU) of 87.2%, accuracy of 97.17%, and recall of 97.15%. When comparing the performance of our proposed model with eight different state-of-the-art image segmentation artificial neural network models, our model performed best in mIOU, Dice similarity coefficient, accuracy, and recall.

Conclusions

Our proposed SCC-Net architecture was able to successfully segment lesions from white light endoscopic images with promising accuracy, with a single model performing well in all upper aerodigestive tracts.

Citation Download Citation

Chien-Yu Huang, Cheng-Che Tsai, Lisa Alice Hwang, Bor-Hwang Kang, Yaoh-Shiang Lin, Hsing-Hao Su, Guan‐Ting Shen, and Jun-Wei Hsieh "SCC-NET: segmentation of clinical cancer image for head and neck squamous cell carcinoma," Journal of Medical Imaging 11(6), 065501 (21 November 2024). https://doi.org/10.1117/1.JMI.11.6.065501

Received: 28 June 2024; Accepted: 23 October 2024; Published: 21 November 2024

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