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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Magnetic Resonance Imaging (MRI) is a key modality in cancer treatment, providing high soft tissue contrast for the visualization of tumors and internal anatomy. Radiotherapy, which is widely used in treatments, requires precise tumor segmentation to ensure targeting the true tumor and minimizing radiation exposure to healthy tissues. In this regard, real-time automatic tumor tracking from cine-MRI can provide accurate tumor localization supporting adaptive radiotherapy. Conventional image registration techniques exhibit limitations when handling large misalignments and high computational demands, unlike deep learning methods, with high learning capabilities and fast inference times. A real-time tumor tracking approach for 2D cine-MRI using deep learning-based deformable image registration, based on an improved TransMorph architecture, is presented. The approach adheres to a two-step training paradigm: (1) unsupervised pretraining on unlabeled patient image pairs, and (2) supervised fine-tuning with segmentation labels. Attention gates are integrated into skip connections to enhance spatial selectivity regarding the most relevant regions for alignment. A composite loss function is utilized, synthesizing boundary-weighted Dice, adaptive MSE, L1 and smooth diffusion and edge-based regularization. Both overlap and distance-based metrics were computed to assess the model\u2019s segmentation accuracy in the registration of various frames within the patients. The proposed model achieved DSC\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$93.42\\% \\pm 5\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>93.42<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                            <mml:mo>\u00b1<\/mml:mo>\n                            <mml:mn>5<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , 95HD\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$2.75 \\pm 3.24$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>2.75<\/mml:mn>\n                            <mml:mo>\u00b1<\/mml:mo>\n                            <mml:mn>3.24<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    mm, 50HD\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$0.88 \\pm 0.40$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>0.88<\/mml:mn>\n                            <mml:mo>\u00b1<\/mml:mo>\n                            <mml:mn>0.40<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    mm. Extensive benchmarking demonstrated that the proposed framework consistently achieved superior performance compared to the TransMorph model, its variants, and other existing state-of-the-art image registration approaches. Experimental results indicate that this technique may serve as a critical tool for the advancement of MRI-guided radiotherapy.\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-02091-y","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T16:28:35Z","timestamp":1787761715000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-Guided TransMorph for Real-Time Tumor Tracking in Cine-MRI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2832-3747","authenticated-orcid":false,"given":"Konstantinos","family":"Georgas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodoros P.","family":"Vagenas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis A.","family":"Vezakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Kakkos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George K.","family":"Matsopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"2091_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109970","volume":"190","author":"E Lombardo","year":"2024","unstructured":"Lombardo, E., Dhont, J., Page, D., et\u00a0al.: Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. 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