Browsing ICR Divisions by author "Kalantar, Reza"
Now showing items 1-4 of 4
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Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.
Kalantar, R; Lin, G; Winfield, JM; Messiou, C; Lalondrelle, S; et al. (MDPI, 2021-10-22)The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ... -
CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN).
Kalantar, R; Messiou, C; Winfield, JM; Renn, A; Latifoltojar, A; et al. (FRONTIERS MEDIA SA, 2021-07-30)BACKGROUND: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes ... -
Deep Learning for Automatic Segmentation, Treatment Planning and MRI Quantitative Analysis of Cervical Cancer
Koh D-M; Kalantar, R; Koh, D-M (Institute of Cancer Research (University Of London), 2024-07-25)Cervical cancer is a threatening global health concern for women, typically treated with radiation therapy (RT) in advanced stages, traditionally planned using computed tomography (CT) scans. However, advancements in medical ... -
Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.
Kalantar, R; Hindocha, S; Hunter, B; Sharma, B; Khan, N; et al. (NATURE PORTFOLIO, 2023-06-29)Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world ...