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arXiv · 2507.12591

CT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling

Trong-Thang Pham·Akash Awasthi·Saba Khan·Esteban Duran Marti·Tien-Phat Nguyen·Khoa Vo·Minh Tran·Ngoc Son Nguyen·Cuong Tran Van·Yuki Ikebe·Anh Totti Nguyen·Anh Nguyen·Zhigang Deng·Carol C. Wu·Hien Van Nguyen·Ngan Le
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Abstract

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.

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Trong-Thang Pham, Akash Awasthi, Saba Khan, Esteban Duran Marti, Tien-Phat Nguyen, Khoa Vo, Minh Tran, Ngoc Son Nguyen, Cuong Tran Van, Yuki Ikebe, Anh Totti Nguyen, Anh Nguyen, Zhigang Deng, Carol C. Wu, Hien Van Nguyen, Ngan Le. 2025-07-16. CT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling. https://arxiv.org/abs/2507.12591

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