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A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13626))

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Head and Neck (HN) cancer has the sixth highest incidence rate of all malignancies worldwide. One of the two main curative treatments for this malignancy is radiotherapy, whose delivery depends on accurate contouring of the primary tumor and affected lymph nodes among other structures. In this study, we present a transfer learning-based approach for the automatic primary tumor and lymph nodes segmentation in fused positron emission tomography (PET) and computed tomography (CT) images belonging to the HECKTOR challenge dataset. Transfer learning is performed from the Genesis Chest CT model, a publicly available 3D U-net, pre-trained on chest CT scans. Three-fold cross-validation is employed during training, so that, on each fold, two different binary segmentation models are chosen, one for the primary tumor and one for the lymph nodes. During testing, majority voting is applied. Our results show promising performance on the training and validation cohorts, while moderate performance was observed in the test cohort.

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  1. Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71, 209–249 (2021)

    Google Scholar 

  2. van der Veen, J., Gulyban, A., Nuyts, S.: Interobserver variability in delineation of target volumes in head and neck cancer. Radiother. Oncol. 137, 9–15 (2019)

    Article  Google Scholar 

  3. La Macchia, M., et al.: Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat. Oncol. 7, 160 (2012)

    Article  Google Scholar 

  4. Oreiller, V., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Med. Image Anal. 77, 102336 (2022)

    Article  Google Scholar 

  5. Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 1–37. Springer, Cham (2022).

    Chapter  Google Scholar 

  6. Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)

    Article  Google Scholar 

  7. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS 2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017).

    Chapter  Google Scholar 

  8. Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks (2017).

  9. Abraham, N., Khan, N.M.: A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation (2018).

  10. Saeed, N., Al Majzoub, R., Sobirov, I., Yaqub, M.: An ensemble approach for patient prognosis of head and neck tumor using multimodal data. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 278–286. Springer, Cham (2022).

    Chapter  Google Scholar 

  11. Pavic, M., et al.: Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol. 57, 1070–1074 (2018)

    Article  Google Scholar 

  12. Alzubaidi, L., et al.: Novel transfer learning approach for medical imaging with limited labeled data. Cancers (Basel). 13, 1590 (2021)

    Google Scholar 

  13. Matsoukas, C., Haslum, J.F., Sorkhei, M., Soderberg, M., Smith, K.: What makes transfer learning work for medical images: feature reuse & other factors. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 9215–9224. IEEE (2022)

    Google Scholar 

  14. Alzubaidi, L., et al.: Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. 10, 4523 (2020)

    Google Scholar 

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Correspondence to Agustina La Greca Saint-Esteven .

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La Greca Saint-Esteven, A., Motisi, L., Balermpas, P., Tanadini-Lang, S. (2023). A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham.

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