TY - GEN

T1 - Automatic Retinogeniculate Visual Pathway Identification Based on Data-driven Fiber Clustering and Anatomical Constrains

AU - Zeng, Qingrun

AU - Zhang, Jiawei

AU - Chen, Shengwei

AU - Xie, Lei

AU - Huang, Jiahao

AU - He, Jianzhong

AU - Pan, Yiang

AU - Yu, Jiangli

AU - Hu, Qiming

AU - Amelina, Evgeniya

AU - Amelin, Mihail

AU - Feng, Yuanjing

N1 - Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - The visual system relies heavily on the retinogeniculate visual pathway (RGVP). The identification of RGVP has been accomplished using tractography based on diffusion MRI. Yet, owing to its extremely curved course and complex anatomical setting, RGVP tractography still presents obstacles. The huge false-positive fibers produced by RGVP tractography, which necessitate the labor-intensive hand-drawing of ROIs for fiber filtering, are one of the main obstacles. In order to enable automatic RGVP detection in dMRI tractography, we have proposed a pipeline. First, we created an RGVP atlas based on tractography. Using high-resolution data from 50 cases, multi-fiber unscented Kalman filter tractography was implemented in this study. Then, using a common space created from the 50 tractography cases, we applied data-driven fiber clustering to put nearby fibers with comparable trajectory into one cluster. The RGVP annotation of tractography atlas was done by two qualified anatomists. Second, 50 testing data were used to identify subject-specific RGVP using the RGVP atlas. Also, we developed a deep learning model to assist in screening RGVP clusters and reduce the false positive fibers in subject-specific RGVP. In terms of identification rate, hausdorff distance, and visualization, experimental findings demonstrated that our automatic identification results have perfect colocalization with expert manual identification.

AB - The visual system relies heavily on the retinogeniculate visual pathway (RGVP). The identification of RGVP has been accomplished using tractography based on diffusion MRI. Yet, owing to its extremely curved course and complex anatomical setting, RGVP tractography still presents obstacles. The huge false-positive fibers produced by RGVP tractography, which necessitate the labor-intensive hand-drawing of ROIs for fiber filtering, are one of the main obstacles. In order to enable automatic RGVP detection in dMRI tractography, we have proposed a pipeline. First, we created an RGVP atlas based on tractography. Using high-resolution data from 50 cases, multi-fiber unscented Kalman filter tractography was implemented in this study. Then, using a common space created from the 50 tractography cases, we applied data-driven fiber clustering to put nearby fibers with comparable trajectory into one cluster. The RGVP annotation of tractography atlas was done by two qualified anatomists. Second, 50 testing data were used to identify subject-specific RGVP using the RGVP atlas. Also, we developed a deep learning model to assist in screening RGVP clusters and reduce the false positive fibers in subject-specific RGVP. In terms of identification rate, hausdorff distance, and visualization, experimental findings demonstrated that our automatic identification results have perfect colocalization with expert manual identification.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85175560024&origin=inward&txGid=5cc169ad79e1d8f0994e89bd49bf8f9a

UR - https://www.mendeley.com/catalogue/d2f4d8f6-39d9-3fda-b9e1-7808118dd365/

U2 - 10.23919/ccc58697.2023.10239759

DO - 10.23919/ccc58697.2023.10239759

M3 - Conference contribution

SN - 9789887581543

SP - 8015

EP - 8020

BT - Chinese Control Conference, CCC

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -