Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image Analysis (TIA), MICCAI 201

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

TB detection using modified Local Binary Pattern features

Abstract: This paper explores a computer-aided detection scheme to aid radiologists in making a higher percentage of correct diagnoses when analysing chest radiographs. The approach undertaken in the detection process is to use several proprietary image processing algorithms to adjust, segment and classify a radiograph. Firstly, a Difference of Gaussian (DoG) energy normalisation method is applied to the image. By doing this, the effect of differing equipment and calibrations is normalised. Thereafter, the lung area is detected using Active Shape Models (ASMs). Once identified, the lungs are analysed using Local Binary Patterns (LBPs). This technique is combined with a probability measure that makes use of the the locations of known abnormalities in the training dataset. The results of the segmentation when compared to ground truth masks achieves an overlap segmentation accuracy of 87,598±3,986%. The challenges faced during classification are also discussed

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system