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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2021

29.03.2021

Cross-Modality Breast Image Translation with Improved Resolution Using Generative Adversarial Networks

verfasst von: Akanksha Sharma, Neeru Jindal

Erschienen in: Wireless Personal Communications | Ausgabe 4/2021

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Abstract

Unpaired cross domain medical image translation is a challenging problem as the target image modality cannot be mapped directly from the input data distribution. The best approach used till date, was by Cycle generative adversarial network, which utilized the cycle consistency loss to perform the task. Although efficient, still the resultant image size was small and blurry. Recent trends show that due to change in lifestyle and increased exposure to carcinogens in different forms has increased occurrence of cancer. Statistics show that every one in eight women might develop breast cancer at some stage of her life. Hence, this paper focuses on a combination of two GANs, CycleGAN and Super resolution GAN is used in two stages to obtain translated breast images with improved resolution. The proposed model is tested on images of breast cancer patients to obtain CT Scan using PET scan and vice versa so that the patients are not exposed to an extremely potent dose of radiation. In order to ensure the presence of tumour in the estimated image, a simplified U-net feature extractor is also used. Quantitative studies are carried out for both the stages of simulation to establish the efficiency of the proposed model.
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Metadaten
Titel
Cross-Modality Breast Image Translation with Improved Resolution Using Generative Adversarial Networks
verfasst von
Akanksha Sharma
Neeru Jindal
Publikationsdatum
29.03.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08376-5

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