Pancreatic Cystic Neoplasms Segmentation Network Combining Dual Decoding and Global Attention Upsampling Modules

Autor: Dai Junlong, He Cong, Wu Jie, Bian Yun
Jazyk: čínština
Rok vydání: 2024
Předmět:
Zdroj: Chinese Journal of Magnetic Resonance, Vol 41, Iss 2, Pp 151-161 (2024)
Druh dokumentu: article
ISSN: 1000-4556
DOI: 10.11938/cjmr20233073
Popis: The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.
Databáze: Directory of Open Access Journals