Deep Learning-Based Auto-Segmentation of Planning Target Volume for
  Total Marrow and Lymph Node Irradiation
Ricardo Coimbra Brioso Damiano Dei Nicola Lambri Daniele Loiacono Pietro Mancosu Marta Scorsetti
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.