Name
Title | ||
|---|---|---|
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry. |
Abstract | ||
|---|---|---|
Lung nodules are commonly detected in screening for patients with a risk for lung cancer. Though the status of large nodules can be easily diagnosed by fine needle biopsy or bronchoscopy, small nodules are often difficult to classify on computed tomography (CT). Recent works have shown that shape analysis of lung nodules can be used to differentiate benign lesions from malignant ones, though existing methods are limited in their sensitivity and specificity. In this work we introduced a new 3D shape analysis within the framework of differential geometry to calculate the Wasserstein distance between benign and malignant lung nodules to derive an accurate classification scheme. The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods. In the process of deformation, the area-distortion factor gives a probability measure on the unit sphere, which forms the Wasserstein space. From known cases of benign and malignant lung nodules, we can calculate a unique optimal mass transport map between their correspondingly deformed Wasserstein spaces. This transportation cost defines the Wasserstein distance between them and can be used to classify new lung nodules into either the benign or malignant class. To the best of our knowledge, this is the first work that utilizes Wasserstein distance for lung nodule classification. The advantages of Wasserstein distance are it is invariant under rigid motions and scalings, thus it intrinsically measures shape distance even when the underlying shapes are of high complexity, making it well suited to classify lung nodules as they have different sizes, orientations, and appearances. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Quantitative Methods | Pattern recognition,Probability measure,Classification scheme,Mass transport,Artificial intelligence,Invariant (mathematics),Differential geometry,Congruence (geometry),Mathematics,Machine learning,Shape analysis (digital geometry),Unit sphere |
DocType | Volume | Citations |
Journal | abs/1807.00094 | 0 |
PageRank | References | Authors |
0.34 | 3 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Zhang | 1 | 134 | 38.40 |
| Qian-Li Ma | 2 | 134 | 23.45 |
| Chengfeng Wen | 3 | 6 | 3.84 |
| Hai Chen | 4 | 0 | 0.68 |
| Deruo Liu | 5 | 2 | 0.70 |
| Xianfeng Gu | 6 | 2997 | 189.71 |
| Jie He | 7 | 19 | 7.35 |
| Xiaoyin Xu | 8 | 0 | 0.34 |