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Updated: Sep 9, 2025

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在CT图像上进行瘤细分的双视角解网络

Xinya Gan¹, Sheng Zhu², Yuan Zhang³

¹Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China; Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, China.

Neural networks : the official journal of the International Neural Network Society

|September 4, 2025

概括

这项研究引入了双视角脱网络 (DDNet) 以改善瘤细分. 通过解物体特征和精细化边界,DDNet提高了准确性,超过了现有的方法.

科学领域:

医学成像
计算机视觉
人工智能

背景情况:

瘤细分面临着不可预测的位置,物体相似性和可变边界等挑战.
目前的方法往往无法有效应对所有这些挑战.
对象不可知或单个视角的方法限制了细分性能.

研究的目的:

提出一种新的双视角脱网络 (DDNet),以加强瘤细分.
解决处理复杂细分场景的现有方法的局限性.
提高医疗图像中脏瘤的准确性和精度.

主要方法:

开发了双视角解网络 (DDNet),包括一个双视角解模块 (DDM) 和一个边缘精制模块 (ERM).
DDM使用多尺度解分支 (MDB) 进行机身/边缘解,以及对象间解分支 (ODB).
该ERM利用边界特征来改进低级编码器特征,提高边界精度.

主要成果:

在KiTS19,KiTS21和KAT-Seg数据集上,DDNet实现了卓越的性能.
达到了86.08% (KiTS19),85.45% (KiTS21) 和88.03% (KAT-Seg) 的子得分.
超过11种最先进的方法,分别为1.63%,0.72%和1.72%.

结论:

DDNet有效地解决了瘤细分方面的挑战.
拟议的双视角解和边缘精细化策略显著提高了细分精度.
DDNet代表了自动瘤细分技术的重大进步.

关键词:

卷积神经网络双视角解边缘精细化瘤细分

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