End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.Comment: Accepted to European Conference on Computer Vision (ECCV) 202

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Early detection of patients at risk of acute renal failure after natural disasters

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Epidemiologic aspects of the Bam earthquake in Iran: the nephrologic perspective

Background: Acute renal failure is a serious, preventable, and potentially reversible midterm complication after mass disasters. In 2003, an earthquake struck Bam, Iran. This article studies the epidemiologic aspects of the earthquake from a nephrologic perspective. Methods: A questionnaire was sent to the reference hospitals. The resulting database of 2,086 traumatized patients hospitalized in the first 10 days was analyzed. Results: Mean age was 29.0 +/- 15.6 years. Compared with the resident population, the percentage of patients was lower among children and teenagers younger than 15 years and higher among young and middle-aged adults (P<0.001). There was no significant difference between mean ages of patients with acute renal failure and other patients. Time under the rubble was longer for patients with acute renal failure (6.2 +/- 4.1 versus 2.1 +/- 3.9 hours; P<0.001). These patients were hospitalized later (3.1 +/- 2.8 versus 1.5 +/- 1.7 days after the disaster; P<0.001) and longer (16.7 +/- 12.8 versus 12.5 +/- 11.3 days; P<0.001). Sepsis (11.6% versus 0.5%), disseminated intravascular coagulation (7.3% versus 0.3%), adult respiratory distress syndrome (9.1% versus 1.4%), fasciotomy (38.9% versus 1.9%), amputation (6.1% versus 0.5%), and death (12.7% versus 1.9%) were markedly more frequent among patients with acute renal failure (P<0.001 for all). Conclusion: Hospitalized patients were mostly young and middle-aged adults. Patients with acute renal failure were entrapped longer and hospitalized later and for longer periods. Medical complications, surgical procedures, and mortality were greater in the latter group. Early extrication and quick hospitalization with appropriate multidisciplinary care are cornerstones to prevent acute renal failure and Its subsequent mortality In earthquake conditions