Auto-segmentation of thoracic brachial plexuses for radiation therapy planning.

Recently, deep learning networks have achieved considerable success in segmenting organs in medical images. Several methods have used volumetric information with deep networks to achieve segmentation accuracy. However, these networks suffer from interference, risk of overfitting, and low accuracy as a result of artifacts, in the case of very challenging objects like the brachial plexuses.

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring.

Background: Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end-to-end deep learning (DL) networks, are weak in garnering high-level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge.

Object recognition in medical images via anatomy-guided deep learning.

Purpose: Despite advances in deep learning, robust medical image segmentation in the presence of artifacts, pathology, and other imaging shortcomings has remained a challenge. In this paper, we demonstrate that by synergistically marrying the unmatched strengths of high-level human knowledge (i.e.

Exploring spinal muscular atrophy and its impact on functional status: Indian scenario.

The present study aimed to find out the effect of disease-related impairments on functional status in individuals with spinal muscular atrophy and identify perceived barriers to undergo physiotherapy. The cross-sectional observational study was conducted on 90 participants from January to March 2018 using validated patient-reported questionnaire via electronic mail, along with Fatigue Severity Scale and ACTIVLIM. Results revealed that difficulty in sitting was due to scoliosis (36%) and muscle weakness (23%), the latter also contributing toward difficulty in standing and walking (59%).

Adipose tissue quantification and primary graft dysfunction after lung transplantation: The Lung Transplant Body Composition study.

Background: Obesity is associated with an increased risk of primary graft dysfunction (PGD) after lung transplantation. The contribution of specific adipose tissue depots is unknown.

Methods: We performed a prospective cohort study of adult lung transplant recipients at 4 U.

AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.

Contouring (segmentation) of Organs at Risk (OARs) in medical images is required for accurate radiation therapy (RT) planning. In current clinical practice, OAR contouring is performed with low levels of automation. Although several approaches have been proposed in the literature for improving automation, it is difficult to gain an understanding of how well these methods would perform in a realistic clinical setting.

Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.

Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object.

Auto-contouring via Automatic Anatomy Recognition of Organs at Risk in Head and Neck Cancer on CT images.

Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H&N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images.

Image Quality and Segmentation.

Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem.

Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates.

Purpose: Overweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed.