Wu Shu, Yu Hang, Li Cuiping, Zheng Rencheng, Xia Xueqin, Wang Chengyan, Wang He
Zhiyu Software Information Co., Ltd., Shanghai 200030, China.
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
Diagnostics (Basel). 2023 Jul 27;13(15):2504. doi: 10.3390/diagnostics13152504.
Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.
肝脏肿瘤语义分割是医学图像分析中的一项关键任务，需要多种磁共振成像（MRI）模态。本文提出了一种新颖的从粗到精的融合分割方法，用于检测和分割各种大小的小肝脏肿瘤。为了提高小肝脏肿瘤的分割精度，该方法结合了一个检测模块和一个CSR（卷积-挤压与激励-残差）模块，该模块包括一个卷积块、一个SE（挤压与激励）模块和一个用于精细分割的残差模块。与传统的单阶段端到端网络相比，该方法表现出卓越的性能。我们为该方法收集了一个包含218名患者、共3605个肿瘤的肝脏MRI私人数据集，其中包括3273个小于3.0厘米的肿瘤。该数据集中识别出五种类型的肝脏肿瘤：肝细胞癌（HCC）；肝转移瘤；胆管癌（ICC）；肝囊肿；以及肝血管瘤。结果表明，该方法优于单分割网络3D UNet和nnU-Net，以及3D UNet和nnU-Net与nnDetection的融合网络。所提出的架构在一个由44幅图像组成的测试集上进行了评估，平均骰子相似系数（DSC）和召回率分别为86.9%和86.7%，与比较方法相比提高了1%。更重要的是，与现有方法相比，我们提出的方法在分割尺寸小于10毫米的小物体方面表现出了领先的性能，获得了85.3%的骰子分数和87.5%的恶性肿瘤检测率。
