Li Xianan, Jia Lecheng, Lin Fengyu, Chai Fan, Liu Tao, Zhang Wei, Wei Ziquan, Xiong Weiqi, Li Hua, Zhang Min, Wang Yi
Department of Radiation Oncology, Peking University People's Hospital, Beijing, China.
Radiotherapy laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
J Appl Clin Med Phys. 2024 Feb 22;25(3):e14296. doi: 10.1002/acm2.14296.
In radiotherapy, magnetic resonance (MR) imaging has higher contrast for soft tissues compared to computed tomography (CT) scanning and does not emit radiation. However, manual annotation of the deep learning-based automatic organ-at-risk (OAR) delineation algorithms is expensive, making the collection of large-high-quality annotated datasets a challenge. Therefore, we proposed the low-cost semi-supervised OAR segmentation method using small pelvic MR image annotations.
We trained a deep learning-based segmentation model using 116 sets of MR images from 116 patients. The bladder, femoral heads, rectum, and small intestine were selected as OAR regions. To generate the training set, we utilized a semi-supervised method and ensemble learning techniques. Additionally, we employed a post-processing algorithm to correct the self-annotation data. Both 2D and 3D auto-segmentation networks were evaluated for their performance. Furthermore, we evaluated the performance of semi-supervised method for 50 labeled data and only 10 labeled data.
The Dice similarity coefficient (DSC) of the bladder, femoral heads, rectum and small intestine between segmentation results and reference masks is 0.954, 0.984, 0.908, 0.852 only using self-annotation and post-processing methods of 2D segmentation model. The DSC of corresponding OARs is 0.871, 0.975, 0.975, 0.783, 0.724 using 3D segmentation network, 0.896, 0.984, 0.890, 0.828 using 2D segmentation network and common supervised method.
The outcomes of our study demonstrate that it is possible to train a multi-OAR segmentation model using small annotation samples and additional unlabeled data. To effectively annotate the dataset, ensemble learning and post-processing methods were employed. Additionally, when dealing with anisotropy and limited sample sizes, the 2D model outperformed the 3D model in terms of performance.
在放射治疗中，与计算机断层扫描（CT）相比，磁共振（MR）成像对软组织具有更高的对比度，且不发出辐射。然而，基于深度学习的自动危及器官（OAR）轮廓描绘算法的人工标注成本高昂，这使得收集大规模高质量标注数据集成为一项挑战。因此，我们提出了一种使用少量盆腔MR图像标注的低成本半监督OAR分割方法。
我们使用来自116例患者的116组MR图像训练了一个基于深度学习的分割模型。选择膀胱、股骨头、直肠和小肠作为OAR区域。为了生成训练集，我们采用了半监督方法和集成学习技术。此外，我们使用了一种后处理算法来校正自标注数据。对二维和三维自动分割网络的性能进行了评估。此外，我们评估了半监督方法在50个标注数据和仅10个标注数据情况下的性能。
仅使用二维分割模型的自标注和后处理方法时，分割结果与参考掩码之间膀胱、股骨头、直肠和小肠的骰子相似系数（DSC）分别为0.954、0.984、0.908、0.852。使用三维分割网络时，相应OAR的DSC分别为0.871、0.975、0.975、0.783、0.724；使用二维分割网络和普通监督方法时，相应OAR的DSC分别为0.896、0.984、0.890、0.828。
我们的研究结果表明，使用少量标注样本和额外的未标注数据来训练多OAR分割模型是可行 的。为了有效地标注数据集，采用了集成学习和后处理方法。此外，在处理各向异性和样本量有限的情况时，二维模型在性能方面优于三维模型。
