Heydarheydari Sahel, Birgani Mohammad Javad Tahmasebi, Rezaeijo Seyed Masoud
Department of Medical Imaging and Radiation Sciences, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Pol J Radiol. 2023 Aug 14;88:e365-e370. doi: 10.5114/pjr.2023.130815. eCollection 2023.
Accurately segmenting head and neck cancer (HNC) tumors in medical images is crucial for effective treatment planning. However, current methods for HNC segmentation are limited in their accuracy and efficiency. The present study aimed to design a model for segmenting HNC tumors in three-dimensional (3D) positron emission tomography (PET) images using Non-Local Means (NLM) and morphological operations.
The proposed model was tested using data from the HECKTOR challenge public dataset, which included 408 patient images with HNC tumors. NLM was utilized for image noise reduction and preservation of critical image information. Following pre-processing, morphological operations were used to assess the similarity of intensity and edge information within the images. The Dice score, Intersection Over Union (IoU), and accuracy were used to evaluate the manual and predicted segmentation results.
The proposed model achieved an average Dice score of 81.47 ± 3.15, IoU of 80 ± 4.5, and accuracy of 94.03 ± 4.44, demonstrating its effectiveness in segmenting HNC tumors in PET images.
The proposed algorithm provides the capability to produce patient-specific tumor segmentation without manual interaction, addressing the limitations of current methods for HNC segmentation. The model has the potential to improve treatment planning and aid in the development of personalized medicine. Additionally, this model can be extended to effectively segment other organs from limited annotated medical images.
在医学图像中准确分割头颈癌（HNC）肿瘤对于有效的治疗计划至关重要。然而，目前用于HNC分割的方法在准确性和效率方面存在局限性。本研究旨在设计一种使用非局部均值（NLM）和形态学操作在三维（3D）正电子发射断层扫描（PET）图像中分割HNC肿瘤的模型。
使用来自HECKTOR挑战公共数据集的数据对所提出的模型进行测试，该数据集包括408例患有HNC肿瘤的患者图像。NLM用于图像降噪和保留关键图像信息。预处理后，使用形态学操作评估图像内强度和边缘信息的相似性。Dice分数、交并比（IoU）和准确率用于评估手动和预测的分割结果。
所提出的模型平均Dice分数为81.47±3.15，IoU为80±4.5，准确率为94.03±4.44，证明了其在PET图像中分割HNC肿瘤的有效性。
所提出的算法能够在无需人工干预的情况下生成针对患者的肿瘤分割，解决了当前HNC分割方法的局限性。该模型有可能改善治疗计划并有助于个性化医学的发展。此外，该模型可以扩展以有效地从有限的标注医学图像中分割其他器官。
