neuro-medical-imaging

Tumor segmentation in Liver

The segmentation of the liver and its lesions on medical images enables oncologists to diagnose liver cancer correctly and to evaluate patient reaction to therapy. A fully automatic method to segment the liver and locate its unhealthy tissues is a useful instrument for diagnosing hepatic illnesses and evaluating their action to the medicines. We aim to propose an automatic method to segment liver and its lesions in Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs).

Tumor segmentation in Liver

Tumor segmentation in Liver

Spine Curvature Estimation

Adolescent Idiopathic Scoliosis (AIS) shows in adults as an abnormal spine curvature. Precise automated assessment of the Cobb angle that quantitatively evaluates scoliosis plays a significant part in the diagnosis and therapy of scoliosis. By inspiring through the architecture and popularity of Convolutional Neural Networks (CNNs) in the field of deep learning, we aim to propose a novel automated method to find the Cobb angles for spine curvature estimation that will help in the assessment of scoliosis.

Spine Curvature Estimation

Pneumonia Identification

Over 15% of deaths including children under age 5 are caused by pneumonia globally. We trained a deep learning model for the identification and localization of pneumonia in Chest X-Rays (CXRs) images. Our identification model is based on Mask-RCNN, a deep neural network approach that incorporated local and global features for pixel-wise segmentation.

Pneumonia Identification

Segmentation Tasks

Rapid development in the field of medical imaging are radically changing medicine. Determining the existence or severity of the disease will affect a patient’s clinical care or outcome status in research. During radiotherapy planning, accurate segmentation of medical images is a main step in contouring. We performed the following segmentation tasks in this regard:

  • Lung Segmentation

One of the significant steps in automatic chest X-ray images assessment is to correctly identify the limits of the lung. We put an effort to extract the lung boundary and introduces lung segmentation using rule-based methods such as adaptive thresholding in Chest X-rays.

  • Brain Tumor Segmentation

For tumor segmentation in brain, we devised different rule-based approaches in image processing including adaptive thresholding and K-Means clustering. The thresholding was performed on Magnetic Resonance Imaging (MRI) scans.

 

Segmentation Tasks