Spinal cord gray matter segmentation using deep dilated convolutions

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure

Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.Comment: 15 pages, 9 figure

Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To enforce safety, traditional planning approaches rely on handcrafted rules to generate trajectories. Machine learning-based systems, on the other hand, scale with data and are able to learn more complex behaviors. However, they often ignore that agents and self-driving vehicle trajectory distributions can be leveraged to improve safety. In this paper, we propose modeling a distribution over multiple future trajectories for both the self-driving vehicle and other road agents, using a unified neural network architecture for prediction and planning. During inference, we select the planning trajectory that minimizes a cost taking into account safety and the predicted probabilities. Our approach does not depend on any rule-based planners for trajectory generation or optimization, improves with more training data and is simple to implement. We extensively evaluate our method through a realistic simulator and show that the predicted trajectory distribution corresponds to different driving profiles. We also successfully deploy it on a self-driving vehicle on urban public roads, confirming that it drives safely without compromising comfort. The code for training and testing our model on a public prediction dataset and the video of the road test are available at https://woven.mobi/safepathne

Hydatid disease of the liver: thirty years of surgical experience.

Hydatid disease of the liver is a relatively frequent disease. Although the natural history is almost completely known, several complications may occur. The aim of this study was to show that radical surgical resection of the hepatic hydatid cyst is a safe and very effective technique, based on our results after 30-year experience. A review of most significant studies was carried out. We retrospectively evaluated our surgical cases. From January 1973 to December 2003 we treated 216 patients, 98 males and 118 females. Survival was compared with the Kaplan-Meier test, using log-rank analysis to compare data. Differences with a p value less than 0.05 were considered significant. A total of 279 cysts were excised. We performed pericystectomy in 122 cases, 73 of which closed. We also performed 19 atypical resections, 10 segmentectomies, 20 lobectomies and 2 percutaneous treatments. In more than 90% of cases, preoperative data collection was completed by preoperative ultrasound. The cumulative morbidity was 13%. The recurrence rate amounted to 4.3% at 5 years and 7% at 10 years: of these, 6 occurred after non-radical surgery and 2 after total pericystectomy or liver resection (p < 0.001). Technical advances and accumulated experience permit safe treatment of hepatic hydatid cysts by radical resection, with an almost zero recurrence rate, making it the treatment of choice over partial resection. The utility of percutaneous treatment remains confined to limited indications, such as laparoscopy