Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation
This work addresses the time-consuming and error-prone manual contouring in complex radiotherapy treatments, offering a scalable solution to benefit more patients, though it is incremental as it builds on prior U-Net methods.
The paper tackled the problem of automating the segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation in radiotherapy, using deep learning models based on nnU-Net, and achieved statistically significant improvements in segmentation performance, particularly in challenging areas like lymph nodes.
In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the accurate contouring of the Planning Target Volume (PTV) is crucial. Unfortunately, relying on manual contouring for such treatments is time-consuming and prone to errors. In this paper, we investigate the application of Deep Learning (DL) to automate the segmentation of the PTV in TMLI treatment, building upon previous work that introduced a solution to this problem based on a 2D U-Net model. We extend the previous research (i) by employing the nnU-Net framework to develop both 2D and 3D U-Net models and (ii) by evaluating the trained models on the PTV with the exclusion of bones, which consist mainly of lymp-nodes and represent the most challenging region of the target volume to segment. Our result show that the introduction of nnU-NET framework led to statistically significant improvement in the segmentation performance. In addition, the analysis on the PTV after the exclusion of bones showed that the models are quite robust also on the most challenging areas of the target volume. Overall, our study is a significant step forward in the application of DL in a complex radiotherapy treatment such as TMLI, offering a viable and scalable solution to increase the number of patients who can benefit from this treatment.