DSR: Direct Simultaneous Registration for Multiple 3D Images

Mao, Zhehua; Zhao, Liang; Huang, Shoudong; Fan, Yiting; Lee, Alex

doi:10.1007/978-3-031-16446-0_10

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…Second column: results obtained by aligning the volumes with the automatic registration using the fiducial points annotated by the obstetrician, followed by similarity registration using the mask that contours the brain. Third column: results obtained by aligning the volumes with DSR registration [34] of generating a high amount of training data with reliable ground truth labels. An obstetrician annotated the position of the TV SP by directly manipulating a slicing plane with the joystick and choosing the translation and angle sampling intervals to avoid sampling planes at the edges of the volume containing no information.…”

Section: B Unity Simulator For Standard Plane Annotations and Synthet...mentioning

confidence: 99%

See 1 more Smart Citation

Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain

Di Vece,

Lous,

Dromey

et al. 2024

IEEE Trans. Med. Robot. Bionics

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In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 2.97 mm and 6.63°for translation and rotation, respectively.

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Section: B Unity Simulator For Standard Plane Annotations and Synthet...mentioning

confidence: 99%

“…The third column shows the results obtained with a state-of-the-art intensity registration approach called DSR. Since the method is iterative, we initialized it with the fiducial approach and performed a mono-modal rigid registration of multiple volumes [34].…”

Section: A Volume Registrationmentioning

confidence: 99%