Novel morphometric based classification via diffeomorphic based shape representation using manifold learning.
Novel morphometric based classification via diffeomorphic based shape representation using manifold learning.
复制标题
使用流形学习通过基于微分同胚的形状表示进行新颖的基于形态测量的分类。
DOI：
10.1007/978-3-642-15711-0_82
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Madabhushi,Anant
中科院分区：
文献类型：
--
作者：
Sparks,Rachel;Madabhushi,Anant
解剖结构的形态学可以提供关于疾病的重要诊断信息。形态学的隐式特征，例如轮廓平滑度或周长面积比，已被用于计算机化决策支持分类器的背景下，以帮助疾病诊断。这些特征通常特定于领域和应用（例如，边缘不规则是DCE-MRI上恶性乳腺病变的预测因子）。在本文中，我们提出了一个框架，用于提取基于形态相似性（DBS）的功能，以捕捉形状之间的细微形态差异，可能无法捕捉到的隐式功能。对象形态表示使用中轴模型和对象进行比较，通过确定中轴模型之间的对应关系，使用基于聚类的同构配准方案。为了可视化和分类形态差异，采用流形学习方案（图嵌入）来识别中轴模型相似性之间的非线性依赖关系并计算DBS。我们评估了我们的DBS在两个临床问题上的区别：（a）在一组102张图像上使用腺体形态学对前列腺癌进行不同的Gleason分级，（B）在44项乳腺DCE-MRI研究中进行良性和恶性病变。精确-召回曲线表明，与隐式特征相比，DBS特征能够更好地对属于同一类的形状进行分类。一个支持向量机（SVM）分类器进行训练，以区分不同的类别，利用DBS。SVM在乳腺DCE-MRI上区分良性与恶性病变的准确率为83 ± 4.47%，在数字化组织学上区分中等Gleason等级的前列腺癌的准确率超过80%。
Morphology of anatomical structures can provide important diagnostic information regarding disease. Implicit features of morphology, such as contour smoothness or perimeter-to-area ratio, have been used in the context of computerized decision support classifiers to aid disease diagnosis. These features are usually specific to the domain and application (e.g. margin irregularity is a predictor of malignant breast lesions on DCE-MRI). In this paper we present a framework for extracting Diffeomorphic Based Similarity (DBS) features to capture subtle morphometric differences between shapes that may not be captured by implicit features. Object morphology is represented using the medial axis model and objects are compared by determining correspondences between medial axis models using a cluster-based diffeomorphic registration scheme. To visualize and classify morphometric differences, a manifold learning scheme (Graph Embedding) is employed to identify nonlinear dependencies between medial axis model similarity and calculate DBS. We evaluated our DBS on two clinical problems discriminating: (a) different Gleason grades of prostate cancer using gland morphology on a set of 102 images, and (b) benign and malignant lesions on 44 breast DCE-MRI studies. Precision-recall curves demonstrate DBS features are better able to classify shapes belonging to the same class compared to implicit features. A support vector machine (SVM) classifier is trained to distinguish between different classes utilizing DBS. SVM accuracy was 83 ±4.47 % for distinguishing benign from malignant lesions on breast DCE-MRI and over 80% in distinguishing between intermediate Gleason grades of prostate cancer on digitized histology.
登录
查看更多内容
DOI：
--
发表时间：
2006-11
期刊：
--
影响因子：
--
作者：
Vincent Arsigny
通讯作者：
Vincent Arsigny
DOI：
10.1142/9789811200137_0003
发表时间：
2018-01
期刊：
Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore
影响因子：
--
作者：
Hsi-Wei Hsieh;N. Charon
通讯作者：
Hsi-Wei Hsieh;N. Charon
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda
影响因子：
2.1
作者：
Charon, Nicolas;Trouve, Alain
通讯作者：
Trouve, Alain