Core Concepts
This study evaluates and compares the performance of various deep learning architectures and training strategies for automated segmentation of cancer lesions in PET/CT images, with a focus on whole-body and head-and-neck tumor detection.
Abstract
This study explores the application of deep learning techniques for automated segmentation of cancer lesions in PET/CT imaging. The authors analyzed datasets from the AutoPET and HECKTOR challenges, evaluating the performance of popular single-step segmentation architectures like U-Net, UNETR, and V-Net, as well as a two-step segmentation approach.
The key findings are:
Removing cancer-free cases from the training dataset improved the performance of most models on the AutoPET dataset, with the average Dice coefficient increasing from 0.55 to 0.66.
For the HECKTOR dataset, the V-Net and nnU-Net models were the most effective, achieving a mean aggregated Dice coefficient of 0.76.
The two-step segmentation approach using U-Net showed promising results, with the Dice coefficient increasing from 0.58 to 0.60 and the aggregated Dice coefficient from 0.64 to 0.73 compared to the single-step segmentation.
Challenges were encountered in accurately segmenting lesions near metabolically active structures and small-volume tumors, highlighting the need for further advancements in deep learning-based oncological diagnostics.
The study demonstrates the potential of deep learning in precise cancer assessment and could contribute to the development of more targeted and effective cancer diagnosis and treatment techniques.
Stats
"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 results for the HECKTOR dataset ranged from 0.75 to 0.76 for the aggregated Dice coefficient."
Quotes
"Early detection of cancer lesions in patients is crucial for improving survival rates. The prognosis and treatment options depend on the location and stage of the lesions."
"The integration of methods based on deep learning in the analysis of data obtained from PET/CT imaging can significantly increase the efficiency of detection of early-stage small-volume tumors."
"This research offers valuable insights into selecting and configuring neural network models to enhance their diagnostic imaging capabilities. These findings have significant implications for the development of more advanced and accurate diagnostic tools in oncology."