  Chest X-ray radiography is one of the earliest medical imaging technologies
and remains one of the most widely-used for 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. This study, and for the first time, introduces a
multitask deep learning model that generates simultaneously the bone-suppressed
image and the organ-segmented image, enhancing the accuracy of tasks,
minimizing the number of parameters needed by the model and optimizing the
processing time, all by exploiting the interplay between the network parameters
to benefit the performance of both tasks. The architectural design of this
model, which relies on a conditional generative adversarial network, reveals
the process on how the well-established pix2pix network (image-to-image
network) is modified to fit the need for multitasking and extending it to the
new image-to-images architecture. The developed source code of this multitask
model is shared publicly on Github as the first attempt for providing the
two-task pix2pix extension, a supervised/paired/aligned/registered
image-to-images translation which would be useful in many multitask
applications. Dilated convolutions are also used to improve the results through
a more effective receptive field assessment. The comparison with
state-of-the-art algorithms along with ablation study and a demonstration video
are provided to evaluate efficacy and gauge the merits of the proposed
approach.
