Background and purpose: Deep Learning (DL) has been widely explored for
Organs at Risk (OARs) segmentation; however, most studies have focused on a
single modality, either CT or MRI, not both simultaneously. This study presents
a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans
of Head and Neck (H&N) cancer patients.
Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer
patients alongside annotation for 30 OARs from the H&N OAR CT & MR segmentation
challenge dataset were used to develop a segmentation pipeline. After cropping
irrelevant regions, rigid followed by non-rigid registration of CT and MRI
volumes was performed. Two versions of the CT volume, representing soft tissues
and bone anatomy, were stacked with the MRI volume and used as input to an
nnU-Net pipeline. Modality Dropout was used during the training to force the
model to learn from the different modalities. Segmentation masks were predicted
with the trained model for an independent set of 14 new patients. The mean Dice
Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across
these patients to evaluate the pipeline.
Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and
3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this
challenge at the time of submission.
Conclusion: The proposed pipeline achieved the best DS and HD among all
participants of the H&N OAR CT and MR segmentation challenge and sets a new
SOTA for automated segmentation of H&N OARs.

该研究使用深度学习技术对头颈癌患者的 MRI 和 CT 扫描图像中的 30 种危及器官进行分割，达到了目前挑战赛的最佳水平，并在头颈危及器官自动分割方面创造了新的技术前沿。

CT 和 MRI 扫描中头颈癌患者风险器官的自动分割

Automatic segmentation of Organs at Risk in Head and Neck cancer  patients from CT and MRI scans

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。