Due to the high intricacy and inter-patient variability of liver vascular anatomy, planning, and execution of liver resection is challenging. Currently, intraoperative ultrasound (IOUS) is an indispensable imaging modality in the surgical workflow; however, 2D-US imaging modality can be difficult to interpret due to noise and speckle. Determining the exact location of tumors and identifying critical structures to preserve during hepatectomy demands expertise and advanced skills. An AI-based model that can help identify vessels (inferior vena cava (IVC), right hepatic vein (RHV), left hepatic vein (LHV), and middle hepatic vein (MHV)) for real-time IOUS navigation can be of immense value. In this research work, we describe our visual saliency approach that integrates attention blocks into a U-Net model for real-time liver vessel segmentation. The IOUS dataset contains video recordings derived from 12 patients, procured during liver surgery. Experiments involve analyzing video frames using a leave-one-out crossvalidation (LOOCV) approach. To maintain objectivity, strict separation is ensured between training and testing subsets to prevent the concurrent inclusion of the same patients. Additionally, to assess model robustness, we kept video data from two distinct patients in the withheld test dataset. Our proposed DL model achieved a mean dice score of 0.88, 0.72, 0.53, and 0.78 for IVC, RHV, MHV, and LHV respectively using the LOOCV approach. In the future, this research will be extended for real-time segmentation of all vasculature in the liver to include portal vein anatomy, followed by the translation of our model in the operation room during surgery.
Microwave ablation (MWA) is an effective minimally invasive therapy for treating liver cancers, among various local cancer treatments. Computational studies are crucial in simulating MWA, offering insights that may be unreachable from experimental methods. This study investigated the complex relationships between blood perfusion rate and metabolic heat concerning MWA outcomes. 3D patient-specific finite element models are employed, shedding light on the interplay of these parameters and their impact on the efficacy of MWA procedures. Image data from five patients treated with MWA are chosen, creating detailed 3D models of the liver, tumor, and vasculature. Simulations are performed using a triaxial antenna operating at 2.45 GHz, with a standard ablation time of 10 minutes and an input power of 65 Watts. In addition, the microwave antenna mimics the clinical insertion path in each case. The simulation model encompasses the coupled electromagnetic field and bioheat transfer, comprehensively understanding the underlying dynamics. The simulations contain seven distinct blood perfusion rates, both with and without considering metabolic heat. This variation allows for a thorough exploration of their combined impact on tissue damage and tumor destruction throughout MWA therapy. These findings underscore the intricate interplay of factors influencing the outcomes of MWA procedures, emphasizing the importance of comprehensive modeling that incorporates various parameters for accurate predictions.
Image guided percutaneous thermal ablation is widely used for patients with primary or secondary liver tumors who do not qualify for surgical resection. The COVER-ALL study is a randomized Phase II clinical trial that evaluates the impact of using software aid in confirming probe position and ablation coverage. Current practice in the trial involves acquisition of a pre-procedure contrast enhanced computed tomography (CECT) scan for gross tumor volume (GTV) definition and non-contrast CT after probe placement, followed by a biomechanical model-based deformable image registration (Morfeus) between the two scans to map the GTV onto the non-contrast CT for position confirmation. CT scan length that covers the entire liver is needed for Morfeus. In this work, we investigated an alternative workflow with a reduced length non-contrast CT using image padding on the first 50 COVER-ALL trial patients. The full-length non-contrast CT was first cropped to a fixed thickness, ranging from 2.5-7.5 cm, along the GTV. The remaining volume was padded with the CECT based on intensity-based deformable image registration (DIR). Morfeus DIR was performed between the CECT and resultant padded non-contrast CT to map the GTV segmentation from CECT to padded non-contrast CT. The GTV mapping results were compared to the original GTV mapping results performed on the full-length CT. The median mapping differences using cropping thickness of 7.5 cm was 1.2 (0.5-2.3) mm, with only 3 cases having larger than 5 mm discrepancies. The comparable DIR performance suggests the feasibility of acquiring a reduced-length non-contrast CT to maintain image registration accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.