Deep learning presents novel opportunities for the auto-segmentation of gross
tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods
usually necessitate significant manual refinement. This study investigates the
Segment Anything Model (SAM), recognized for requiring minimal human prompting
and its zero-shot generalization ability across natural images. We specifically
examine MedSAM, a version of SAM fine-tuned with large-scale public medical
images. Despite its progress, the integration of multi-modality images (CT,
PET, MRI) for effective GTV delineation remains a challenge. Focusing on SAM's
application in HNC GTV segmentation, we assess its performance in both
zero-shot and fine-tuned scenarios using single (CT-only) and fused
multi-modality images. Our study demonstrates that fine-tuning SAM
significantly enhances its segmentation accuracy, building upon the already
effective zero-shot results achieved with bounding box prompts. These findings
open a promising avenue for semi-automatic HNC GTV segmentation.

本研究通过使用深度学习方法自动分割头颈部癌症的肿瘤体积，并在使用不同模态图像进行 fine-tuning 的情况下对模型性能进行评估。结果表明，在使用边界框提示的情况下，对 SAM 进行 fine-tuning 可以显著提高其分割准确度，为半自动头颈部癌症肿瘤体积分割开辟了一个有前景的途径。

使用 CT、PET 和 MRI 多模态图像的头颈肿瘤分割模型

Segment anything model for head and neck tumor segmentation with CT, PET  and MRI multi-modality images

Convolutional neural networks (CNNs) are increasingly being used to automate
segmentation of organs-at-risk in radiotherapy. Since large sets of highly
curated data are scarce, we investigated how much data is required to train
accurate and robust head and neck auto-segmentation models. For this, an
established 3D CNN was trained from scratch with different sized datasets
(25-1000 scans) to segment the brainstem, parotid glands and spinal cord in
CTs. Additionally, we evaluated multiple ensemble techniques to improve the
performance of these models. The segmentations improved with training set size
up to 250 scans and the ensemble methods significantly improved performance for
all organs. The impact of the ensemble methods was most notable in the smallest
datasets, demonstrating their potential for use in cases where large training
datasets are difficult to obtain.

研究了用不同大小的数据集（25-1000 个扫描）训练三维卷积神经网络以在放射治疗中自动分割头颈部器官的精度和鲁棒性，同时还评估了多种集成技术来改善这些模型的性能，结果表明集成技术对于较小的数据集的影响最为明显，并具有使用困难训练集的潜力。

培训数据集大小和集成推论策略对头颈自动分割的影响

The impact of training dataset size and ensemble inference strategies on head and neck auto-segmentation

Over half a million individuals are diagnosed with head and neck cancer each
year worldwide. Radiotherapy is an important curative treatment for this
disease, but it requires manual time consuming delineation of radio-sensitive
organs at risk (OARs). This planning process can delay treatment, while also
introducing inter-operator variability with resulting downstream radiation dose
differences. While auto-segmentation algorithms offer a potentially time-saving
solution, the challenges in defining, quantifying and achieving expert
performance remain. Adopting a deep learning approach, we demonstrate a 3D
U-Net architecture that achieves expert-level performance in delineating 21
distinct head and neck OARs commonly segmented in clinical practice. The model
was trained on a dataset of 663 deidentified computed tomography (CT) scans
acquired in routine clinical practice and with both segmentations taken from
clinical practice and segmentations created by experienced radiographers as
part of this research, all in accordance with consensus OAR definitions. We
demonstrate the model's clinical applicability by assessing its performance on
a test set of 21 CT scans from clinical practice, each with the 21 OARs
segmented by two independent experts. We also introduce surface Dice similarity
coefficient (surface DSC), a new metric for the comparison of organ
delineation, to quantify deviation between OAR surface contours rather than
volumes, better reflecting the clinical task of correcting errors in the
automated organ segmentations. The model's generalisability is then
demonstrated on two distinct open source datasets, reflecting different centres
and countries to model training. With appropriate validation studies and
regulatory approvals, this system could improve the efficiency, consistency,
and safety of radiotherapy pathways.

采用深度学习方法，训练了一种 3D U-Net 架构的自动分割算法，成功实现了 21 个头颈部放射敏感器官的自动分割，同时引入了表面 Dice 相似系数（surface DSC）一种能更好地反映纠正自动分割错误的临床任务的新指标，表明该模型在不同研究中心和国家具有较强的泛化能力，并可通过适当的验证研究和监管审批实现提高放射治疗路径的效率、一致性和安全性的目标。