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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2507.12591 |
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| _version_ | 1866911061138997248 |
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| author | Pham, Trong-Thang Awasthi, Akash Khan, Saba Marti, Esteban Duran Nguyen, Tien-Phat Vo, Khoa Tran, Minh Nguyen, Ngoc Son Van, Cuong Tran Ikebe, Yuki Nguyen, Anh Totti Nguyen, Anh Deng, Zhigang Wu, Carol C. Van Nguyen, Hien Le, Ngan |
| author_facet | Pham, Trong-Thang Awasthi, Akash Khan, Saba Marti, Esteban Duran Nguyen, Tien-Phat Vo, Khoa Tran, Minh Nguyen, Ngoc Son Van, Cuong Tran Ikebe, Yuki Nguyen, Anh Totti Nguyen, Anh Deng, Zhigang Wu, Carol C. Van Nguyen, Hien Le, Ngan |
| contents | Understanding radiologists' eye movement during Computed Tomography (CT) reading is crucial for developing effective interpretable computer-aided diagnosis systems. However, CT research in this area has been limited by the lack of publicly available eye-tracking datasets and the three-dimensional complexity of CT volumes. To address these challenges, we present the first publicly available eye gaze dataset on CT, called CT-ScanGaze. Then, we introduce CT-Searcher, a novel 3D scanpath predictor designed specifically to process CT volumes and generate radiologist-like 3D fixation sequences, overcoming the limitations of current scanpath predictors that only handle 2D inputs. Since deep learning models benefit from a pretraining step, we develop a pipeline that converts existing 2D gaze datasets into 3D gaze data to pretrain CT-Searcher. Through both qualitative and quantitative evaluations on CT-ScanGaze, we demonstrate the effectiveness of our approach and provide a comprehensive assessment framework for 3D scanpath prediction in medical imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling Pham, Trong-Thang Awasthi, Akash Khan, Saba Marti, Esteban Duran Nguyen, Tien-Phat Vo, Khoa Tran, Minh Nguyen, Ngoc Son Van, Cuong Tran Ikebe, Yuki Nguyen, Anh Totti Nguyen, Anh Deng, Zhigang Wu, Carol C. Van Nguyen, Hien Le, Ngan Computer Vision and Pattern Recognition Understanding radiologists' eye movement during Computed Tomography (CT) reading is crucial for developing effective interpretable computer-aided diagnosis systems. However, CT research in this area has been limited by the lack of publicly available eye-tracking datasets and the three-dimensional complexity of CT volumes. To address these challenges, we present the first publicly available eye gaze dataset on CT, called CT-ScanGaze. Then, we introduce CT-Searcher, a novel 3D scanpath predictor designed specifically to process CT volumes and generate radiologist-like 3D fixation sequences, overcoming the limitations of current scanpath predictors that only handle 2D inputs. Since deep learning models benefit from a pretraining step, we develop a pipeline that converts existing 2D gaze datasets into 3D gaze data to pretrain CT-Searcher. Through both qualitative and quantitative evaluations on CT-ScanGaze, we demonstrate the effectiveness of our approach and provide a comprehensive assessment framework for 3D scanpath prediction in medical imaging. |
| title | CT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.12591 |