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

arXiv: https://arxiv.org/abs/1906.10089 · HTML: https://mlsanity.com/paper/1906.10089/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Automated Generation of Accurate & Fluent Medical X-ray Reports](https://mlsanity.com/paper/2108.12126/) (2021)
- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [ChrSNet: Chromosome Straightening using Self-attention Guided Networks](https://mlsanity.com/paper/2207.00147/) (2022)
- [BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning](https://mlsanity.com/paper/2207.03210/) (2022)
- [Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography](https://mlsanity.com/paper/2307.11513/) (2023)
- [Feasibility Assessment of Multitasking in MRI Neuroimaging Analysis: Tissue Segmentation, Cross-Modality Conversion and Bias correction](https://mlsanity.com/paper/2105.14986/) (2021)
- [Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey](https://mlsanity.com/paper/2106.00997/) (2021)

## Abstract (arXiv)

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.