An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI.

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains.

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation.

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use.

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning.

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.

Novel CT-Based Objective Imaging Biomarkers of Long-Term Radiation-Induced Lung Damage.

Purpose: Recent improvements in lung cancer survival have spurred an interest in understanding and minimizing long-term radiation-induced lung damage (RILD). However, there are still no objective criteria to quantify RILD, leading to variable reporting across centers and trials. We propose a set of objective imaging biomarkers for quantifying common radiologic findings observed 12 months after lung cancer radiation therapy.

NiftyNet: a deep-learning platform for medical imaging.

Background And Objectives: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups.