Unspecified Michael Roumeliotis Profile

Michael Roumeliotis

at Johns Hopkins University

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Proceedings Article | 29 March 2024 Presentation + Paper

Intraoperative MRI-guided cervical cancer brachytherapy with automatic tissue segmentation using dual convolution-transformer network and real-time needle tracking

Gayoung Kim, Majd Antaki, Ehud Schmidt, Michael Roumeliotis, Akila Viswanathan, Junghoon Lee

Proceedings Volume 12928, 1292817 (2024) https://doi.org/10.1117/12.3005475

KEYWORDS: Image segmentation, Magnetic resonance imaging, Cervical cancer, Automatic tracking, Deep learning

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Interstitial brachytherapy is widely used for cervical cancer treatment. Accurate image-guidance of brachytherapy needle insertion should include delineation of the target tumor and organs-at-risk (OARs), and real-time visualization of each needle tip as it is advanced towards the target. While CT and MRI are typically used for treatment planning, they are not readily available for intraoperative image-guidance. Lack of robust means to visualize needles along with the patient’s anatomy during the procedure poses challenges in accurate needle placement. Furthermore, OARs and tumor contouring is time-consuming, therefore is not performed until all catheters are placed. We developed an MRI-guidance system that integrates tools providing automatic segmentation of OARs and the high-risk clinical-target-volume (HR-CTV), and real-time active needle tracking. The segmentation module comprises a coarse segmentation step for organ localization, followed by fine segmentation models separately trained for every OARs and HR-CTV. The HR-CTV segmentation module first detects the tumor size and then performs size-dependent segmentation. The needle-tracking module communicates with active stylets, and displays the stylet-tip location and orientation on the MRI in real-time. These modules were incorporated into a treatment planning system to enable MRI-guidance and online treatment planning. The segmentation models were developed using 213 MRIs, and the system was validated in 5 cervical cancer cases, demonstrating its clinical utility in increasing procedure efficiency. Dice similarity values between the automatic segmentation and an expert’s revised contours of the bladder, rectum, sigmoid, and HR-CTV were 0.94, 0.92, 0.84, and 0.70, respectively. Furthermore, the size-dependent HR-CTV segmentation outperformed a single-model segmentation.

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