Abstract：Due to the complexity of medical imaging and the high heterogeneity of the surface of gliomas, image segmentation of human brain gliomas is one of the most challenging tasks in medical image analysis. This paper aimed to improve the UNet++ medical image segmentation network, the improved network can fuse coarse-grained semantics and fine-grained semantics at full scale. Experiments were performed on 335 images obtained from the public BraTS brain tumor segmentation data set, using 2D and 3D comparative segmentation experiments to comprehensively evaluate the segmentation performance of the improved network and compare the segmentation results with the results of UNet, UNet++, and UNet3+ medical image segmentation networks. Among the four indicators of Dice similarity coefficient (DSC), 95% Hausdorff surface distance (HSD95),sensitivity, and positive predictive value (PPV), 2D contrast segmentation achieved the mean values of the indicators of 83.70%, 1.7, 88.40%, 84.96% respectively; the mean values of the 3D contrast segmentation reached 90.79%, 0.242, 91.23%, 91.06% respectively. Compared with the segmentation result indicators of the other three networks, in the 2D comparison experiment, DSC increased by 1.82% on average, HSD95 decreased by 0.35 on average, sensitivity increased by 2.13% on average, and PPV increased by 0.80% on average; in the 3D comparison experiment, DSC increased by 2.78% on average, HSD95 decreased by 0.076 on average, Sensitivity increased by 3.81% on average, and PPV increased by 0.68% on average. It was shown that the proposed algorithm made the segmentation result of glioma and the gold standard overlap more in the region, and completed the segmentation of glioma better. It is expected to help neurosurgeons to more precisely separate brain tumors and tissues around the brain and achieve rapid computer diagnosis and treatment.
[1] Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging 2015,34(10), 1993-2024. [2] 张玲华.神经胶质瘤的诊断与治疗现状[J].实用医技杂志,2007(24):3274-3276. [3] 徐庆中,卢德宏,宣琪.神经胶质瘤病理学研究进展[J].实用肿瘤杂志,2004(6):458-462. [4] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2015,39(4):640-651. [5] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015:234-241. [6] Zhou Zongwei. UNet++: a nested U-net architecture for medical image segmentation [C]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Granada: Springer, 2018:3-11. [7] Chen Liangjie, Apandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(4):834-848. [8] Huang Huimin, Lin Lanfen. UNet 3+: a full-scale connected unet for medical image segmentation[C] //2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona:ICASSP, 2020:1055-1059. [9] Bakas S, Akbari H, Sotiras A,et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features.[DB/OL].https://pubmed.ncbi.nlm.nih.gov/28872634.html/, 2017-09-05/2021-09-06. [10] Bakas S, Reyes M, Jakab A, et al. Identifying the best machine learning algorithms for brain tumor segmentation.[EB/OL]. https://arxiv.org/abs/1811.02629, 2019-04-23/2021-09-06. [11] Bakas S, Akbari H, Sotiras A, et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection.[EB/OL].https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=24282666.html,2021-05-25/2021-09-06. [12] Wang Hongzhi, Jung WS, Das SR, et al. Multi-atlas segmentation with joint label fusion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,35(3):611-623. [13] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C]// IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015:3431-3440. [14] Wu Jiong, Zhang Yue, Tang Xiaoying. A multi-atlas guided 3D fully convolutional network for MRI-based subcortical segmentation[C] //2019 IEEE 16th International Symposium on Biomedical Imaging(ISBI 2019). Venice: IEEE,2019:705-708. [15] 陈龙,赵巍.一种改进的自适应分段线性变换算法[J].激光与红外,2020,50(8):1020-1024. [16] Lu Yan, Qin Xuejun, Fan Haoyi, et al. WBC-Net: A white blood cell segmentation network based on unet++ and resnet[J]. Applied Soft Computing Journal,2021, 101(12):107-118. [17] 梁芳烜,杨锋,卢丽云,等.基于卷积神经网络的脑肿瘤分割方法综述[J].计算机工程与应用,2021,57(7):34-43. [18] 潘燕七,陈睿,张旭,等.基于浅层与深层特征融合的胃癌前疾病识别[J].中国生物医学工程学报,2020,39(4):413-421. [19] Chen Hao, Qi Dou, Yu Lequan, et al. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images[J]. Neurolmage,2018,170:446-455. [20] Zikic D, Ioannou Y, Brown M, et al. Segmentation of brain tumor tissues with convolutional neural networks[C] //MICCAI Workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS). Boston: MICCAI, 2014:36-39. [21] Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging, 2016, 35(5):1240-1251. [22] Havaei M, Davy A, Warde-Farley D,et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 2017, 35:18-31. [23] 江宗康,吕晓钢,张建新,等.MRI脑肿瘤图像分割的深度学习方法综述[J].中国图象图形学报,2020,25(2):215-228. [24] 殷宁波,黄冕,刘利军,等. MS-UNet++: 基于改进UNet++的视网膜血管分割[J]. 光电子·激光,2021,32(1):35-41. [25] 侯阿临,吴浪,孙弘建,等. 一种基于UNet++优化和权值预算的脑肿瘤分割算法[P]. 吉林省:CN112598656A,2021-04-02.
