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2023

GazeSAM: What You See is What You Segment

Wang, Bin, Aboah, Armstrong, Zhang, Zheyuan et al.

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This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation.

We present the \textbf{GazeSAM} system to enable radiologists to collect segmentation masks by simply looking at the region of interest during image diagnosis.
The proposed system tracks radiologists' eye movement and utilizes the eye-gaze data as the input prompt for SAM, which automatically generates the segmentation mask in real time.
This study is the first work to leverage the power of eye-tracking technology and SAM to enhance the efficiency of daily clinical practice.

GazeSAM: What You See is What You Segment · Around

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Florez, E., Fatemi, A., Claudio, P.P., Howard, C.M.: Emergence of radiomics: novel methodology identifying imaging biomarkers of disease in diagnosis, response, and progression. SM journal of clinical and medical imaging 4
2018
Earlier work this paper cites.
Khosravan, N., Celik, H., Turkbey, B., Jones, E.C., Wood, B., Bagci, U.: A collaborative computer aided diagnosis (c-cad) system with eye-tracking, sparse attentional model, and deep learning. Medical image analysis 51
2019
Earlier work this paper cites.
Wood, B.J., Celik, H., Bagci, U., Turkbey, I.B.: Eye tracking applications in computer aided diagnosis and image processing in radiology (Nov 17 2020), uS Patent 10,839,520
Original
2020
Earlier work this paper cites.

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2023
Cited alongside, same era.

Then

Ma, J., Wang, B.: Segment anything in medical images (2023)
2023
Closest in time.
Roy, S., Wald, T., Koehler, G., Rokuss, M.R., Disch, N., Holzschuh, J., Zimmerer, D., Maier-Hein, K.H.: Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model. arXiv preprint arXiv:2304.05396 (2023)
Original
2023
Closest in time.

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