Semi-supervised Semantic Segmentation of Multiple Lumbosacral Structures on CT

Liu, Huaqing; Xiao, Haoping; Luo, Lin; Feng, Chaobo; Yin, Bangde; Wang, Dongdong; Li, Yufeng; He, Shisheng; Fan, Guoxin

doi:10.1007/978-3-030-39752-4_5

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pauline et al [28] trained a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network to detect and correct higher-order inconsistencies between ground truth and the map generated by the segmentation net. Huaqing Liu et al [45] proposed semi-cGAN, based on CGAN, to segment lumbosacral structures on thin-layer computed tomography with a few labeled data. Souly et al [46] leveraged a massive amount of available unlabeled or weakly labeled data and non-real images created through the GAN to achieve semisupervised learning, and subsequently, Hung W C [47] made improvements based on it.…”

Section: Related Workmentioning

confidence: 99%

Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

et al. 2021

View full text Add to dashboard Cite

Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.

show abstract

Section: Related Workmentioning

confidence: 99%