Cancer is one of the leading causes of death globally, and early diagnosis is crucial for patient survival. Deep learning algorithms have great potential for automatic cancer analysis. Artificial intelligence has achieved high performance in recognizing and segmenting single lesions. However, diagnosing multiple lesions remains a challenge. This study examines and compares various neural network architectures and training strategies for automatically segmentation of cancer lesions using PET/CT images from the head, neck, and whole body. The authors analyzed datasets from the AutoPET and HECKTOR challenges, exploring popular single-step segmentation architectures and presenting a two-step approach. The results indicate that the V-Net and nnU-Net models were the most effective for their respective datasets. The results for the HECKTOR dataset ranged from 0.75 to 0.76 for the aggregated Dice coefficient. Eliminating cancer-free cases from the AutoPET dataset was found to improve the performance of most models. In the case of AutoPET data, the average segmentation efficiency after training only on images containing cancer lesions increased from 0.55 to 0.66 for the classic Dice coefficient and from 0.65 to 0.73 for the aggregated Dice coefficient. The research demonstrates the potential of artificial intelligence in precise oncological diagnostics and may contribute to the development of more targeted and effective cancer assessment techniques.

翻译：癌症是全球主要致死原因之一，早期诊断对患者生存至关重要。深度学习算法在癌症自动分析方面具有巨大潜力。人工智能在识别和分割单个病灶方面已取得显著性能，但多病灶诊断仍具挑战性。本研究系统评估并比较了多种神经网络架构和训练策略，用于基于头部、颈部及全身PET/CT图像的癌症病灶自动分割。作者分析了AutoPET和HECKTOR挑战数据集，探索了主流单步分割架构并提出两步法。结果表明，V-Net和nnU-Net模型分别在各自数据集中表现最优。HECKTOR数据集的聚合Dice系数结果范围为0.75至0.76。去除AutoPET数据集中无癌症病例可提升多数模型性能。在AutoPET数据中，仅使用含癌病灶图像训练后，经典Dice系数的平均分割效率从0.55提升至0.66，聚合Dice系数从0.65提升至0.73。研究展示了人工智能在精准肿瘤诊断中的潜力，有望推动更具靶向性且高效的癌症评估技术发展。