In this paper, we propose an end-to-end deep neural network for solving the
problem of imbalanced large and small organ segmentation in head and neck (HaN)
CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more
than 10 organs-at-risk (normal organs) need to be precisely segmented in
advance. However, the size ratio between large and small organs in the head
could reach hundreds. Directly using such imbalanced organ annotations to train
deep neural networks generally leads to inaccurate small-organ label maps. We
propose a novel end-to-end deep neural network to solve this challenging
problem by automatically locating, ROI-pooling, and segmenting small organs
with specifically designed small-organ sub-networks while maintaining the
accuracy of large organ segmentation. A strong main network with densely
connected atrous spatial pyramid pooling and squeeze-and-excitation modules is
used for segmenting large organs, where large organs' label maps are directly
output. For small organs, their probabilistic locations instead of label maps
are estimated by the main network. High-resolution and multi-scale feature
volumes for each small organ are ROI-pooled according to their locations and
are fed into small-organ networks for accurate segmenting small organs. Our
proposed network is extensively tested on both collected real data and the
\emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows
superior performance compared with state-of-the-art segmentation methods.

本文提出了一种端到端的神经网络模型用于解决头颈 CT 影像中不平衡大小器官分割的问题，并通过特定的小器官网络准确地分割小器官，最终在收集的实际数据和 MICCAI 头颈自动分割竞赛数据集上具有优秀的表现。

FocusNet：头颈 CT 图像的端到端深度神经网络，用于不平衡的大和小器官分割

FocusNet: Imbalanced Large and Small Organ Segmentation with an  End-to-End Deep Neural Network for Head and Neck CT Images

Methods: Our deep learning model, called AnatomyNet, segments OARs from head
and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT
images as input and generating masks of all OARs of interest in one shot.
AnatomyNet is built upon the popular 3D U-net architecture, but extends it in
three important ways: 1) a new encoding scheme to allow auto-segmentation on
whole-volume CT images instead of local patches or subsets of slices, 2)
incorporating 3D squeeze-and-excitation residual blocks in encoding layers for
better feature representation, and 3) a new loss function combining Dice scores
and focal loss to facilitate the training of the neural model. These features
are designed to address two main challenges in deep-learning-based HaN
segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic
nerves) occupying only a few slices, and b) training with inconsistent data
annotations with missing ground truth for some anatomical structures.
Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI
Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to
evaluate the performance of AnatomyNet. The objective is to segment nine
anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right,
parotid gland left, parotid gland right, submandibular gland left, and
submandibular gland right. Compared to previous state-of-the-art results from
the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient
by 3.3% on average. AnatomyNet takes about 0.12 seconds to fully segment a head
and neck CT image of dimension 178 x 302 x 225, significantly faster than
previous methods. In addition, the model is able to process whole-volume CT
images and delineate all OARs in one pass, requiring little pre- or
post-processing.
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利用深度学习模型 - AnatomyNet 可以自动分割头颈部 CT 图像中的 OARs，该模型的 3 个核心特点包括全容积编码方式、编码层中的 3D squeeze-and-excitation 剩余块、以及基于 Dice 分数和 focal loss 的新损失函数。相较于之前的分割方法，AnatomyNet 显著提高了分割精度（Dice 相似系数增加了 3.3%），同时可在 0.12 秒内处理完整个头颈部 CT 图像，并且无需或很少需要前 / 后处理步骤。