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Conference Papers Year : 2023

Anatomical Landmark Detection for Initializing US and MR Image Registration

Abstract

Targeted MR/ultrasound (US) fusion biopsy is a technology made possible by overlaying ultrasound images of the prostate with MRI sequences for the visualization and the targeting of lesions. However, US and MR image registration requires a good initial alignment based on manual anatomical landmark detection or prostate segmentation, which are time-consuming and often challenging during an intervention. We propose to explicitly and automatically detect anatomical landmarks of prostate in both modalities to achieve initial registration. Firstly, we train a deep neural network to detect three anatomical landmarks for both MR and US images. Instead of relying on heatmap regression or coordinate regression using a fully connected layer, we regress coordinates of landmarks directly by introducing a differentiable layer in U-Net. After being trained and validated on 900 and 152 cases, the proposed method predicts landmarks within a Mean Radial Error (MRE) of 5.55 ± 2.63 mm and 5.77 ± 2.67 mm in 263 test cases for US and MR images, separately. Secondly, least-squares fitting is applied to calculate a rough rigid transformation based on detected anatomical landmarks. Surface registration error (SRE) of 6.62 ± 3.97 mm and Dice score of 0.77 ± 0.11 are achieved, which are both comparable metrics in clinical setting when comparing with previous method.
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Dates and versions

hal-04189905 , version 1 (29-08-2023)

Identifiers

  • HAL Id : hal-04189905 , version 1

Cite

Zhijie Fang, Hervé Delingette, Nicholas Ayache. Anatomical Landmark Detection for Initializing US and MR Image Registration. MICCAI ASMUS 2023 - 4th International Workshop of Advances in Simplifying Medical UltraSound - a workshop held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2023, Vancouver, Canada. ⟨hal-04189905⟩
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