A Multi-model Organ Segmentation Method Based on Abdominal Ultrasound Image

被引：0

作者：

Li Dandan ^{[1
]}

Miao Huanhuan ^{[2
]}

Jiang Yu ^{[1
]}

Shen Yi ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Astronaut, Control Sci & Engn, Harbin, Peoples R China

[2] Harbin Med Univ, Affiliated Hosp 2, In Patient Ultrasound Dept, Harbin, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 15TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP 2020) | 2020年

关键词：

Ultrasound Images; Abdominal Organ; Semantic Segmentation; U-Net;

D O I：

10.1109/icsp48669.2020.9320910

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A multi-model organ segmentation method was proposed in this paper. The modified U-Net deep neural network model and simplified DenseNet deep neural network model was used in our method to segment and classify abdominal organs. Preliminary semantic segmentation results were corrected combined with medical prior knowldege and the characteristics of video frame correlation, so that the fine segmentation of abdominal organs based on ultrasound images could be realized. TPR and SIR of our method reached 0.8172 and 0.7971, superior to similar algorithms in terms of both visual effect and evaluation indexes.

引用

页码：504 / 509

页数：6

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