Deep Active Contours Using Locally Controlled Distance Vector Flow

Active contours Model (ACM) has been extensively used in computer vision and image processing. In recent studies, Convolutional Neural Networks (CNNs) have been combined with active contours replacing the user in the process of contour evolution and image segmentation to eliminate limitations associated with ACM's dependence on parameters of the energy functional and initialization. However, prior works did not aim for automatic initialization which is addressed here. In addition to manual initialization, current methods are highly sensitive to initial location and fail to delineate borders accurately. We propose a fully automatic image segmentation method to address problems of manual initialization, insufficient capture range, and poor convergence to boundaries, in addition to the problem of assignment of energy functional parameters. We train two CNNs, which predict active contour weighting parameters and generate a ground truth mask to extract Distance Transform (DT) and an initialization circle. Distance transform is used to form a vector field pointing from each pixel of the image towards the closest point on the boundary, the size of which is equal to the Euclidean distance map. We evaluate our method on four publicly available datasets including two building instance segmentation datasets, Vaihingen and Bing huts, and two mammography image datasets, INBreast and DDSM-BCRP. Our approach outperforms latest research by 0.59 ans 2.39 percent in mean Intersection-over-Union (mIoU), 7.38 and 8.62 percent in Boundary F-score (BoundF) for Vaihingen and Bing huts datasets, respectively. Dice similarity coefficient for the INBreast and DDSM-BCRP datasets is 94.23% and 90.89%, respectively indicating our method is comparable to state-of-the-art frameworks.

Machine Learning Distinguishes Neurosurgical Skill Levels in a Virtual Reality Tumor Resection Task

Background: Virtual reality simulators and machine learning have the potential to augment understanding, assessment and training of psychomotor performance in neurosurgery residents. Objective: This study outlines the first application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality neurosurgical task. Methods: Twenty-three neurosurgeons and senior neurosurgery residents comprising the "skilled" group and 92 junior neurosurgery residents and medical students the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Over 100 features were extracted and 68 selected using t-test analysis. These features were provided to 4 classifiers: K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Equal Error Rate was used to assess classifier performance. Results: Ratios of train set size to test set size from 10% to 90% and 5 to 30 features, chosen by the forward feature selection algorithm, were employed. A working point of 50% train to test set size ratio and 15 features resulted in an equal error rates as low as 8.3% using the Fuzzy K-Nearest Neighbors classifier. Conclusion: Machine learning may be one component helping realign the traditional apprenticeship educational paradigm to a more objective model based on proven performance standards. Keywords: Artificial intelligence, Classifiers, Machine learning, Neurosurgery skill assessment, Surgical education, Tumor resection, Virtual reality simulation