Deep-learning Segmentation of Small Volumes in CT images for
  Radiotherapy Treatment Planning
Jianxin Zhou Kadishe Fejza Massimiliano Salvatori Daniele Della Latta Gregory M. Hermann Angela Di Fulvio
Our understanding of organs at risk is progressing to include physical small
tissues such as coronary arteries and the radiosensitivities of many small
organs and tissues are high. Therefore, the accurate segmentation of small
volumes in external radiotherapy is crucial to protect them from
over-irradiation. Moreover, with the development of the particle therapy and
on-board imaging, the treatment becomes more accurate and precise. The purpose
of this work is to optimize organ segmentation algorithms for small organs. We
used 50 three-dimensional (3-D) computed tomography (CT) head and neck images
from StructSeg2019 challenge to develop a general-purpose V-Net model to
segment 20 organs in the head and neck region. We applied specific strategies
to improve the segmentation accuracy of the small volumes in this anatomical
region, i.e., the lens of the eye. Then, we used 17 additional head images from
OSF healthcare to validate the robustness of the V Net model optimized for
small-volume segmentation. With the study of the StructSeg2019 images, we found
that the optimization of the image normalization range and classification
threshold yielded a segmentation improvement of the lens of the eye of
approximately 50%, compared to the use of the V-Net not optimized for small
volumes. We used the optimized model to segment 17 images acquired using
heterogeneous protocols. We obtained comparable Dice coefficient values for the
clinical and StructSeg2019 images (0.61 plus/minus 0.07 and 0.58 plus/minus
0.10 for the left and right lens of the eye, respectively)