Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short-and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier

Multiscale combination of physically-based registration and deformation modeling

Abstract 1 In this paper we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea o f our approach is that a nite element (FEM) model can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdor distance to recover corresp ondences. The method is evaluated with resp ect to speed, accur acy, and noise sensitivity. A dvantages of the pr oposed a p p r oach ar e demonstr ated using man-made elastic materials and human skin motion. Experiments with regular grid featur esare used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown that the new method does not requir ea grid and can adapt the model to available object featur es

Correspondence A Curvature-Based Approach to Terrain Recognition

A6stract-This paper describes an algorithm which uses a Gaussian and mean curvature profile for extracting special points on the terrain, and then uses these points for recognition of particular regions of the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature image, the points of maximum and minimum curvature are extracted and used for matching. The stability of the position of these points in the presence of noise and with resampling is investigated. The input for this algorithm is 3-D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting which is invariant to coordinate system transformation is suggested and implemented. The real terrain data used in our experiments are compiled by the U.S. Army Engineer Topographic Laboratories, Fort Belvoir, VA. The algorithm is tested with and without the presence of noise and its performance is described. Index Terms-Computer vision, range imaging, shape and 3-D description, 3-D representation and recognition, visual navigation

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

AbstractPurposeTo assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methodsFor 20 NSCLC patients (stages Ib–IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.ResultsHigh overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2cm3, mean±SD) and manual delineations (81.9±94.1cm3; p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).ConclusionsSemiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors

Motion estimation from three-dimensional data

In this thesis several new algorithms for the matching and motion estimation from 3-D data are introduced. First, various versions of the algorithms are classified into four levels based on the availability of the constraints such as object rigidity, point features, and matching correspondences. Then, at each level, several new algorithms are derived and their performance and applications are presented.The first level is the most primitive form of motion estimation in which rigidity, point features, and matching correspondences are required. To demonstrate such an algorithm the terrain matching problem is considered. Surface curvatures are used to obtain motion invariant feature points which are used for matching and motion estimation. Real range data is used to test algorithm performance.At the second level, the requirement of matching correspondences is removed for the purpose of computation reduction. First, algorithms for motion estimation from 3-D data without correspondences are introduced for both point and line feature points. Motion estimation is performed by fixing three orthonormal vectors to the 3-D set before and after the motion, and recovering motion parameters from positions of those vectors. Then the problem of matching of 3-D sets is considered and matching algorithms are derived. The stability of these algorithms is investigated and tested on the simulated noisy data. Finally, algorithms are used on real data of vehicle motion tracking and automated construction.At the third level, the point-feature requirement is removed in order to further reduce the computational complexity. This modification enables the algorithm to perform motion estimation from continuous image distributions. Both matching and motion estimation algorithms are presented and parameters which affect the stability of these algorithms are investigated.At level four, the rigidity constraint is removed while the requirement of point features and matching correspondences is retained. Algorithms in level four are intended for motion analysis of nonrigid or partially rigid objects and are based on tracking of surface curvatures. Algorithms are developed and applied, first, to the simulated piecewise-rigid and nonrigid object, and then to the human left ventrical data. The heart surface profiles and the surface stretching parameters are reconstructed from 3-D data obtained by the biplane angiography.U of I OnlyETDs are only available to UIUC Users without author permissio

Extracting Nonrigid Motion and 3D Structure of Hurricanes from Satellite Image Sequences without Correspondences

Image sequences capturing Hurricane Luis through meteorological satellites (GOES-8 and GOES-9) are used to estimate hurricane-top heights (structure) and hurricane winds (motion). This problem is difficult not only due to the absence of correspondences but also due to the lack of depth cues in the 2D hurricane images (scaled orthographic projection). In this paper, we present a structure and motion analysis system, called SMAS. In this system, the hurricane images are first segmented into small square areas. We assume that each small area is undergoing similar nonrigid motion. A suitable nonrigid motion model for cloud motion is first defined. Then, non-linear least-square method is used to fit the nonrigid motion model for each area in order to estimate the structure, motion model, and 3D nonrigid motion correspondences. Finally, the recovered hurricane-top heights and winds are presented along with an error analysis. Both structure and 3D motion correspondences are estimated to subpi..

Active Learning to Recognize Multiple Types of Plankton

Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling. 1