|Table of Contents|

Edge Guided 3D CT Image Segmentation of Adrenal Gland(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2025年01期
Page:
93-99
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Edge Guided 3D CT Image Segmentation of Adrenal Gland
Author(s):
Wang Wenjing1; Niu Sijie1; Li Fan2; Cao Caixia3; Cong Wenbin3; Yang Zicheng3
(1.College of Information Science and Engineering,University of Jinan,Jinan 250000,China)
(2.Perception Vision Medical Technologies Co.,Ltd.,Guangzhou 510530,China)
(3.The Affiliated Hospital of Qingdao University,Qingdao 266000,China)
Keywords:
full convolution; Transformer; MedNeXt; unbalanced sample categories; volume segmentation
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2025.01.012
Abstract:
Computed tomography image is the main imaging method to judge the condition of kidney. Doctors can determine the cause of kidney disease by segmenting the adrenal region of interest in the abdominal CT image and calculating the volume,gray value and surface area of the adrenal gland. However,it is time-consuming,tedious and challenging to manually mark the lesion area in the image,and the lesion area is very similar to the surrounding tissue,and the boundary outlined is extremely fuzzy. Therefore,the method adopted in this paper uses a full convolution neural network model MedNeXt — a transformer inspired large core segmentation network to perform volume segmentation on 3D adrenal data. In order to deal with the problem of unbalanced sample categories,this paper also uses symmetrical unified focus loss to replace Dice loss to improve segmentation accuracy. At the same time,considering the problem that it is difficult to distinguish between adrenal tissue and surrounding tissue boundaries,this paper proposes to combine the boundary loss function and the main body loss function to simultaneously monitor the segmentation process,so that the model pays more attention to the details of the boundary,thus improving the model performance and achieving more accurate segmentation results. Finally,experiments show that the method used in this paper achieves the most advanced performance on the adrenal 3D dataset compared with the latest models in recent years.

References:

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Last Update: 2025-02-15