Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Abstract

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widelyused for the diagnosis, screening and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. Although image processing could facilitate computer aided diagnosis, machine learning seems more amenable in dealing with the many parameters one would have to contend with to yield an near optimal classification or decision-making process. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bonesuppressed image and the organ segmented image, minimizing as a consequence the number of parameters the model has to deal with and optimizing the processing time as well; while at the same time exploiting the interplay in these parameters so as to benefit the performance of both tasks. The design architecture of this model, which relies on a conditional generative adversarial network, reveals the process on how we managed to modify the well-established pix2pix network to fit the need for multitasking and hence extending the standard image-to-image network to the new image-to-images architecture. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. A comparison of the proposed approach to state-of-the-art algorithms is provided to gauge the merits of the proposed approach.

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

“…Recently, there have been several works for the radiograph segmentation. The previous researches of accurate radiograph segmentation focused on the segmentation of organs [38,39]. The segmentations of the lungs were used to aid the diagnosis of COVID-19 [15].…”

Bayesian Feature Pyramid Networks for Automatic Multi-label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

“…In particular, we did not experiment with training the models from scratch and used transfer learning. The second limitation of our study is that we did not compare our method to state-of-the-art unsupervised domain adaptation approaches [9,4,11]. However, this would require re-implementation of previously presented methods as our annotations for all the test set differ from all the previously published techniques.…”

“…Chest X-ray Segmentation. The most relevant studies to ours are by Dong et al [9], by Dai et al [7] and also by Elsami et al [11]. They introduced adversarial training to enforce the consistency between the predictions and the ground truth annotations.…”

An Overview of Deep Learning Approaches in Chest Radiograph

“…Eslami et al [62] employed pix2pix network to achieve organ segmentation and bone suppression on a CXR simultaneously. Similar to a typical GAN architecture, pix2pix consists of a generator and critic network.…”