  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.
