November 1, 2021Cancer Management and ResearchOpen Access

Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

View Full Paper

Ask AI

Bookmark

Abstract

Our proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Liu et al. (2021) studied this question.

synapsesocial.com/papers/69d7f6a161e2ce1627d18705 https://doi.org/10.2147/cmar.s330249

View Full Paper

Ask AI

Bookmark

Abstract

Our proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.

Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

Abstract

Discussion

Cite This Study

Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

Abstract

Discussion

Cite This Study

Keep reading

Keep reading