Needle Localization and Segmentation for Radiofrequency Ablation of Liver Tumors under CT Image Guidance

Anh, Le Quoc; Ha, Luu Manh; Walsum, Theo van; Moelker, Adriaan; Hang, Dao Viet; Phương, Phạm Cẩm; Thanh, Vu Duy

doi:10.23919/apsipaasc55919.2022.9980132

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…On the other hand, training 3D deep learning networks for needle segmentation on MR images typically requires large 3D training datasets [24], which may not be available for specific MRI-guided procedures or at specific facilities. Studies of applying 3D deep learning networks for needle segmentation on CT and US images have similarly demonstrated the data-demanding nature of these networks [25,26]. The potentially limited sizes of intra-procedural 3D MR image datasets and the variabilities in the needle feature's location and grayscale appearance in in vivo 3D MRI may lead to insufficient training of the 3D deep learning network and result in inaccurate 3D needle feature segmentation and localization.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI

Zhou,

Li,

Zabihollahy

et al. 2024

Int J CARS

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Purpose Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset. Methods The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p < 0.05 considered significant. Results The average end-to-end computational time for the pipeline was 6 s per 3D volume. The median Dice scores of the 3D Swin UNETR and 2D Swin Transformer in the pipeline were 0.80 and 0.93, respectively. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, respectively. Needle tip localization errors were significantly smaller than human intra-reader variation (median 1.70 mm; p < 0.01). Conclusion The proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions.

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Section: Introductionmentioning

confidence: 99%