计算机科学
分割
转化(遗传学)
人工智能
图像分割
深度学习
参数统计
尺度空间分割
螺旋(铁路)
正规化(语言学)
胰腺癌
计算机视觉
模式识别(心理学)
癌症
数学
医学
数学分析
内科学
统计
基因
化学
生物化学
作者
Xiahan Chen,Zihao Chen,Jun Li,Yudong Zhang,Xiaozhu Lin,Xiaohua Qian
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:41 (1): 75-87
被引量:12
标识
DOI:10.1109/tmi.2021.3104460
摘要
Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both manually annotate and automatically segment cancers. Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. Specifically, a spiral-transformation algorithm with uniform sampling was developed to map 3D images onto 2D planes while preserving the spatial relationship between textures, thus addressing the challenge in effectively applying 3D contextual information in a 2D model. This study is the first to introduce spiral transformation in a segmentation task to provide effective data augmentation, alleviating the issue of small sample size. Moreover, a transformation-weight-corrected module was embedded into the deep learning model to unify the entire framework. It can achieve 2D segmentation and corresponding 3D rebuilding constraint to overcome non-unique 3D rebuilding results due to the uniform and dense sampling. A smooth regularization based on rebuilding prior knowledge was also designed to optimize segmentation results. The extensive experiments showed that the proposed method achieved a promising segmentation performance on multi-parametric MRIs, where T2, T1, ADC, DWI images obtained the DSC of 65.6%, 64.0%, 64.5%, 65.3%, respectively. This method can provide a novel paradigm to efficiently apply 3D information and augment sample sizes in the development of artificial intelligence for cancer segmentation. Our source codes will be released at https://github.com/SJTUBME-QianLab/ Spiral-Segmentation.
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