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)

通过优化 V-Net 模型的器官分割算法，提高小体积器官的分割精度，特别是眼球晶状体的分割，在医学影像中取得了显著的改进。

CT 影像中小体积的深度学习分割在放射治疗计划中的应用

Deep-learning Segmentation of Small Volumes in CT images for  Radiotherapy Treatment Planning

We propose a new framework for processing Fringe Patterns (FP). Our novel
approach builds upon the hypothesis that the denoising and normalisation of FPs
can be learned by a deep neural network if enough pairs of corrupted and ideal
FPs are provided. The main contributions of this paper are the following: (1)
We propose the use of the U-net neural network architecture for FP
normalisation tasks; (2) we propose a modification for the distribution of
weights in the U-net, called here the V-net model, which is more convenient for
reconstruction tasks, and we conduct extensive experimental evidence in which
the V-net produces high-quality results for FP filtering and normalisation. (3)
We also propose two modifications of the V-net scheme, namely, a residual
version called ResV-net and a fast operating version of the V-net, to evaluate
the potential improvements when modify our proposal. We evaluate the
performance of our methods in various scenarios: FPs corrupted with different
degrees of noise, and corrupted with different noise distributions. We compare
our methodology versus other state-of-the-art methods. The experimental results
(on both synthetic and real data) demonstrate the capabilities and potential of
this new paradigm for processing interferograms.

本研究提出了一种基于深度神经网络的新的凹凸条纹图案 FP 去噪和标准化的框架，并使用 U-net，V-net 模型对其进行模拟和改良，对不同噪声和分布的 FP 进行实验评估，证明其重要性和潜力。