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arXiv · 2404.04202

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
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Abstract

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)

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Jianxin Zhou, Kadishe Fejza, Massimiliano Salvatori, Daniele Della Latta, Gregory M. Hermann, Angela Di Fulvio. 2024-04-05. Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning. https://arxiv.org/abs/2404.04202

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