Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network

ABSTRACT We developed a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN). The MTANN is a trainable highly nonlinear filter that can be trained by using input chest radiographs and the corresponding teacher images. We used either the soft-tissue image or the bone image obtained by use of a dual-energy subtraction technique as the teacher image for suppression of ribs in chest radiographs. When the soft-tissue images were used as the teacher images, the MTANN directly produced a "soft-tissue-image-like" image where the contrast of ribs was suppressed. When the bone images were used as the teacher images, the MTANN was able to produce a "bone-image-like" image, and then was subtracted from the corresponding chest radiograph to produce a bone-subtracted image where ribs are suppressed. Thus, the two kinds of rib-suppressed images, i.e., the soft-tissue-image-like image and the bone-subtracted image, could be produced by use of the MTANNs trained with two different teacher images. We applied each of the two trained MTANNs to non-training chest radiographs to investigate the difference between the processed images. The results showed that the contrast of ribs in chest radiographs almost disappeared, and was reduced to less than 10% in both processed images. The contrast of ribs was reduced slightly better in the soft-tissue-image-like images than in the bone-subtracted images, whereas soft-tissue opacities such as lung vessels and nodules were maintained better in the bone-subtracted images. Therefore, the use of the bone images as the teacher images for training the MTANN has produced better rib-suppressed images where soft-tissue opacities were substantially maintained. A method for rib suppression using the MTANN would be useful for radiologists as well as CAD schemes in detection of lung diseases such as nodules in chest radiographs

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A bone suppression model ensemble to improve COVID-19 detection in chest X-rays

Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities like COVID-19 may become difficult due to that they are obscured by the presence of bony structures like the ribs and the clavicles, thereby resulting in screening/diagnostic misinterpretations. Automated bone suppression methods would help suppress these bony structures and increase soft tissue visibility. In this study, we propose to build an ensemble of convolutional neural network models to suppress bones in frontal CXRs, improve classification performance, and reduce interpretation errors related to COVID-19 detection. The ensemble is constructed by (i) measuring the multi-scale structural similarity index (MS-SSIM) score between the sub-blocks of the bone-suppressed image predicted by each of the top-3 performing bone-suppression models and the corresponding sub-blocks of its respective ground truth soft-tissue image, and (ii) performing a majority voting of the MS-SSIM score computed in each sub-block to identify the sub-block with the maximum MS-SSIM score and use it in constructing the final bone-suppressed image. We empirically determine the sub-block size that delivers superior bone suppression performance. It is observed that the bone suppression model ensemble outperformed the individual models in terms of MS-SSIM and other metrics. A CXR modality-specific classification model is retrained and evaluated on the non-bone-suppressed and bone-suppressed images to classify them as showing normal lungs or other COVID-19-like manifestations. We observed that the bone-suppressed model training significantly outperformed the model trained on non-bone-suppressed images toward detecting COVID-19 manifestations.Comment: 29 pages, 10 figures, 4 table