Pulmonary lobe segmentation is an important task for pulmonary disease related Computer Aided Diagnosis systems (CADs). Classical methods for lobe segmentation rely on successful detection of fissures and other anatomical information such as the location of blood vessels and airways. With the success of deep learning in recent years, Deep Convolutional Neural Network (DCNN) has been widely applied to analyze medical images like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which, however, requires a large number of ground truth annotations. In this work, we release our manually labeled 50 CT scans which are randomly chosen from the LUNA16 dataset and explore the use of deep learning on this task. We propose pre-processing CT image by cropping region that is covered by the convex hull of the lungs in order to mitigate the influence of noise from outside the lungs. Moreover, we design a hybrid loss function with dice loss to tackle extreme class imbalance issue and focal loss to force model to focus on voxels that are hard to be discriminated. To validate the robustness and performance of our proposed framework trained with a small number of training examples, we further tested our model on CT scans from an independent dataset. Experimental results show the robustness of the proposed approach, which consistently improves performance across different datasets by a maximum of $5.87\%$ as compared to a baseline model.
FILE - In this June 1, 2014 file photo, Stephanie J. Block attends the Drama Desk Awards in New York. Block, Mandy Gonzalez, Mario Cantone, Robert Creighton and Randy Graff will lend their voices to the fight against the lung-scarring disease pulmonary fibrosis. The seventh annual concert Broadway Belts for PFF! will be held on Feb. 27, 2017 at the Edison Ballroom in Manhattan.
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only.
A group of researchers from Osaka has discovered that physical activity can be beneficial to patients with progressive smoking-induced pulmonary diseases. The team representing Osaka City University's department of respiratory medicine concluded that increased levels of the hormone irisin, released during exercise, may help slow the progress of chronic obstructive pulmonary disease (COPD). COPD is a group of chronic and inflammatory lung diseases that cause limitation in lung airflow and may be life-threatening and progressively lead to death, according to the World Health Organization. They are characterized by symptoms such as breathlessness, excessive production of sputum, a mix of saliva and mucus discharged from the respiratory tract, and chronic coughing. "We found out that exercising can reduce the negative effects of smoking," Kazuhisa Asai, a lecturer for the department of respiratory medicine who was among the researchers, said by telephone.