Superpixel Combining U-NET for Pancreas Segmentation

被引：0

作者：

Cao Z. ^{[1
]}

Qiao N. ^{[1
]}

Bu Q. ^{[1
]}

Feng J. ^{[1
]}

机构：

[1] College of Information Science and Technology, Northwest University, Xi'an

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 10期

关键词：

Dice similarity coefficient (DSC); Pancreas segmentation; Superpixel; U-NET;

D O I：

10.3724/SP.J.1089.2019.17655

中图分类号：

学科分类号：

摘要：

In order to improve the performance of pancreas segmentation, this paper proposes a pancreas segmentation method combined superpixel and U-NET. Firstly, we propose a medical superpixel segmentation method. Then we map and reduce dimensionality to obtain visual summary image according to the result of superpixel segmentation. Finally, we use the visual summary image and superpixel position information as the input of U-NET to obtain the pancreas segmentation result. The experimental results on the NIH pancreas public dataset show that the DSC of this method is 87.9%, which is higher than all current pancreas segmentation methods; and the method is faster than U-NET. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：1777 / 1785

页数：8

共 14 条

[1]

Gonzalez R.C., Woods R.E., Digital Image Processing, pp. 454-467, (2011)

[2]

Farag A., Lu L., Roth H.R., Et al., A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling, IEEE Transactions on Image Processing, 26, 1, pp. 386-399, (2017)

[3]

Cai J.Z., Lu L., Zhang Z.Z., Et al., Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural net-works, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 442-450, (2016)

[4]

Shelhamer E., Long J., Darrell T., Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 4, pp. 640-651, (2017)

[5]

Krahenbuhl P., Koltun V., Efficient inference in fully connected CRFs with gaussian edge potentials

[6]

Roth H.R., Lu L., Lay N., Et al., Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation, Medical Image Analysis, 45, pp. 94-107, (2018)

[7]

Roth H.R., Lu L., Farag A., Et al., Spatial aggregation of holistically-nested networks for automated pancreas segmentation, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 451-459, (2016)

[8]

Zhou Y.Y., Xie L.X., Shen W., Et al., A fixed-point model for pancreas segmentation in abdominal CT scans, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 693-701, (2017)

[9]

Liu Y.J., Liu S., U-net for pancreas segmentation in abdominal CT scans

[10]

Chen J.S., Li Z.Q., Huang B., Linear spectral clustering superpixel, IEEE Transactions on Image Processing, 26, 7, pp. 3317-3330, (2017)

← 1 2 →