Liver Tumor Localization Research Articles

Medical Image Analysis for Liver Tumor Localization and Segmentation using Deep Learning

Medical Image Analysis for Liver Tumor Localization and Segmentation using Deep Learning

A method of three-dimensional non-rigid localization of liver tumors based on structured light

In this paper, a new localization method of liver tumors based on structured light is proposed to achieve the accurate resection of liver tumors. Firstly, the preoperative liver model and intraoperative liver surface are completed respectively based on CT sections and structured light. Secondly, the 3D reconstruction of liver surface based on structured light is used as the reference surface for registration. A common ICP algorithm and an improved ICP algorithm based on statistics are proposed to complete the rough and fine registration of liver surface. In this paper, the theory of PBD is introduced, and the registration results are used to guide the preoperative deformation of the liver model. Finally, the method of 3D non-rigid localization of liver tumors proposed in this paper is verified by experiments. In addition, the effectiveness of the proposed method is compared with that of the tumor localization obtained by the finite element analysis and Coherent point drift methods. The experimental results show that the average localization error of liver tumor is 1.24 mm. The time spent during the entire process of tumor localization is kept under fifteen seconds. The method presented in this paper meets the needs of surgeons in the intraoperative process.

Real-Time 3D Liver Tumor Localization via Combined Optical Surface Imaging and an X-Ray Projection from Arbitrary Imaging Angles

A deep learning (DL)-based, deformable registration-driven liver tumor localization technique was developed for onboard deformable motion tracking. The technique (Surf-X360-Bio) uses in-room optical surface imaging and an x-ray projection at an arbitrary scan angle to solve volumetric liver and liver tumor motion in real-time. Surf-X360-Bio solves the volumetric motion of the liver and localizes the liver tumor, through deforming liver and liver tumor meshes segmented on prior 4D-CTs/CBCTs. It uses real-time onboard information from an optical surface image and a simultaneously-acquired x-ray projection (from an arbitrary scan angle). Surf-X360-Bio localizes tumors via two steps: liver boundary motion estimation and intra-liver motion derivation. Surf-X360-Bio first estimates liver boundary motion by a patient-specific surface imaging model (Surf), utilizing the correlation between the external body surface motion and the internal liver boundary motion. As the correlation can be imperfect, the residual motion estimation errors were corrected by a patient-specific, angle-agnostic x-ray imaging model (X360). X360 deformed the liver boundary to match motion-related imaging features encoded in an arbitrarily-angled x-ray projection, using the Surf output for initialization. X360 adopted a geometry-aware learning module to extract and adapt to angle-varying features of arbitrarily-angled x-ray images, by calculating the projection system matrix of each x-ray image on-the-fly during model training and inference. After the liver boundary motion estimation by Surf and X360, intra-liver deformation was solved by a biomechanical model (Bio) to propagate the liver boundary motion inside to localize the tumors. The DL-based Bio model used domain knowledge of tissue biomechanics and finite element analysis (FEA) to solve intra-liver motion, with a much faster speed than conventional FEA methods to meet the real-time constraint. Surf-X360-Bio was evaluated using a dataset of 34 liver patients. Liver tumor localization accuracy was evaluated by the Dice similarity coefficient (DSC), the 95-percentile Hausdorff distance (HD95), and the center-of-mass error (COME). Using 3,306 motion scenarios spanning the 360 degree x-ray scan angles for each testing patient, Surf-X360-Bio localized the liver tumors to 0.81 (mean) ± 0.16 (s.d.) in DSC, 2.5 ± 1.7 mm in HD95, and 2.1 ± 1.8 mm in COME. In comparison, the prior reference image without deformable registration-driven localization yielded 0.42 ± 0.29 in DSC, 8.1 ± 5.2 mm in HD95, and 8.5 ± 5.2 mm in COME. Via Surf-X360-Bio, the overall inference time was below 230 ms for each case. Combining optical surface imaging and x-ray imaging, Surf-X360-Bio achieved fast (<250 ms inference time), accurate (mean error < 2.1 mm), and robust liver tumor localizations at arbitrary x-ray scan angles for real-time, marker-less 3D deformable motion tracking.

Dual-Ligand-Functionalized Liposomes Based on Glycyrrhetinic Acid and cRGD for Hepatocellular Carcinoma Targeting and Therapy.

Hepatocellular carcinoma (HCC) is one of the most common cancers, with high mortality. Chemotherapy is one of the main treatment options for HCC. However, the high toxicity and poor specificity of chemotherapeutic drugs have limited their clinical application. In this study, dual-ligand liposomes modified with glycyrrhetinic acid (GA) and cyclic arginine-glycine-aspartic acid (cRGD) (GA/cRGD-LP) were designed to target the GA receptor and αvβ3 integrin, respectively. The aim was to develop a highly selective targeted drug delivery system and further enhance the antitumor efficiency of drugs by targeting both hepatic tumor cells and vasculature. A novel lipid conjugate (mGA-DOPE) by coupling dioleoylphosphatidyl ethanolamine (DOPE) with methyl glycyrrhetinic acid (mGA) was synthesized, and its structure was confirmed. The targeting efficiency of GA/cRGD-LP by in vitro cellular uptake and ex vivo imaging was assessed. GA- and cRGD-modified doxorubicin-loaded liposomes (GA/cRGD-LP-DOX) were prepared, and their cytotoxicity in HepG2 and antitumor activity were evaluated. The results showed that the average particle size of the GA/cRGD-LP-DOX was 114 ± 4.3 nm, and the zeta potential was -32.9 ± 2.0 mV. The transmission electron microscopy images showed that the shapes of our liposomes were spherical. cGA/cRGD-LP-DOX displayed an excellent cellular uptake in both HepG2 and human umbilical vein endothelial cells. In the in vivo study, pharmacokinetic parameters indicated that cGA/cRGD-LP can prolong the circulation time of DOX in the blood. GA/cRGD-LP-DOX showed greater inhibition of tumor growth for HepG2-bearing mice than either the single-ligand-modified liposomes or nontargeted liposomes. GA/cRGD-LP-DOX displayed higher liver tumor localization than that of single-ligand-modified liposomes or free DOX. GA/cRGD-LP is a promising drug delivery system for liver cancer targeting and therapy and is worthy of further study.

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection. Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm. Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

Liver Tumor Localization and Size Are No Longer Contraindications in Percutaneous Thermal Ablation in the Era of Stereotactic Navigation Technology

Liver Tumor Localization and Size Are No Longer Contraindications in Percutaneous Thermal Ablation in the Era of Stereotactic Navigation Technology

Deep Learning-Driven Real-Time Liver Tumor Localization via Optical Surface Imaging and Biomechanical Modeling

<h3>Purpose/Objective(s)</h3> To track liver tumors in real time via a deep learning-based framework, by exploring patient-specific correlations between patients' body surface maps and internal anatomical motion. <h3>Materials/Methods</h3> Real-time liver tumor localization was achieved via deformable registration-based motion estimation, in a two-step framework: (a) liver boundary motion estimation via deep features learnt from optical body surface imaging; and (b) intra-liver motion propagation via deep learning-based biomechanical modeling from liver boundary motion. In step (a), a patient-specific, fully-connected convolutional neural network (SurfCNN) predicts the motion of nodes residing on the boundary of a patient-specific reference liver mesh, by extracting saliency features from curvature entropy maps derived out of real-time optical body surface images. Subsequently, step (b) uses the liver boundary motion solved in (a) to infer intra-liver tumor motion, using a U-Net-style model (UNet-Bio) inspired by biomechanical modeling via finite element analysis (FEA). The cascaded framework was evaluated using a dataset of 8 liver cancer patients from our institute. Each patient had a 10-phase, contrast-enhanced 4D-CT set with liver and liver tumor contoured. To generate sufficient motion variations to train and test the patient-specific SurfCNN model, we augmented the dataset of each patient by simulating varying real-time motion patterns and magnitudes. The augmentation was achieved via a principal component analysis-based statistical model, expanding the nine inter-phase deformation-vector-fields (DVFs) of each 4D-CT into 1,728 different motion states including both rigid and deformable motion. Optical surface imaging was simulated from each motion state via extracted body surface maps, while corresponding liver boundary motion was derived via the augmented DVFs to supervise the training of SurfCNN. UNet-Bio, on the other hand, could be trained as a population-based model using supervisions from intra-liver DVFs solved by FEA-based biomechanical modeling. The liver tumor localization accuracy was assessed through Dice similarity coefficient (DSC), Hausdorff distance (HD), and center-of-mass-error (COME), by comparing the augmentation DVF-propagated ‘ground-truth' liver tumor volumes against ones deformed by the cascaded framework. <h3>Results</h3> Tested using 576 unseen scenarios for each patient case, the cascaded SurfCNN and UNet-Bio scheme can localize liver tumors to 0.791(mean) ± 0.141 (s.d.) in DSC, 3.6 ± 1.9 mm in HD, and 2.3 ± 1.7 mm in COME. In comparison, the prior reference image without deformable registration yielded 0.534 ± 0.277 in DSC, 8.3 ± 6.1 mm in HD and 6.8 ± 5.8 mm in COME. The whole model inference time was less than 100 milliseconds, satisfying the temporal constraints of real-time imaging. <h3>Conclusion</h3> The deep learning-based framework allows accurate 3D liver tumor tracking in real-time via non-ionizing, marker-less, and high frame-rate optical surface imaging.

Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.

Despite its prevalence, cone beam computed tomography (CBCT) has poor soft-tissue contrast, making it challenging to localize liver tumors. We propose a patient-specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground-truth MRI. The synthetic MRI demonstrated superb soft-tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Our study demonstrated the feasibility of a patient-specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs.

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

Objective. Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients’ anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection. Approach. Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer. Main results. The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (<1.2 (±1.2) mm average localization error). Significance. The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).

Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board tumor localizations. Biomechanical modeling has also been introduced to fine-tune the intra-liver deformation-vector-fields (DVFs) solved by 2D-3D deformable registration, especially at low-contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone-beam projections, which degrades the accuracy of the intensity-based 2D-3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep-learning (DL)-based method to optimize the liver boundary DVFs after 2D-3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization. The DL-based network was built based on the U-Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D-3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient-specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra-liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D-3D deformable registration, 2D-3D-Bio (2D-3D deformable registration with biomechanical modeling), and DL-Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center-of-mass-errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented "gold-standard" contours. The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D-3D, 2D-3D-Bio, and DL-Bio techniques were 4.7 ± 1.9mm, 2.9 ± 1.0mm, and 1.7 ± 0.4mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6mm, 5.4 ± 1.5mm, and 4.5 ± 1.3mm, respectively. DL-Bio solves a general correlation model to improve the accuracy of the DVFs at the liver boundary. With improved boundary conditions, the accuracy of biomechanical modeling can be further increased for accurate intra-liver low-contrast tumor localization.

Automatic Cone Beam Projection-based Liver Tumor Localization by Deep Learning and Biomechanical Modeling

Accurate on-board liver tumor localization remains a challenge for CBCT-based image-guided radiotherapy, due to tumors’ low-visibility within normal liver tissues. Deformable registration-driven contour propagation from planning CTs may facilitate automatic, accurate tumor localization. We developed a technique combining a population-based liver surface deformation model and a patient-specific, biomechanical modeling-guided algorithm to achieve accurate deformation-driven liver tumor contour propagation and localization. The technique solves the deformation-vector-fields (DVFs) for tumor propagation by three steps: 1. Solving a coarse DVF via 2D-3D deformable registration by intensity-matching DRRs of the deformed planning CT to on-board cone-beam projections; 2. Feeding the DVF into a population-based deep learning model to improve its accuracy around liver surface region; and 3. Applying the accuracy-enhanced liver surface DVF to a patient-specific liver biomechanical model as the boundary condition, to solve intra-liver DVF through finite element analysis. The optimized intra-liver DVF is then applied to the liver tumor contour to propagate it onto the CBCT frame for automatic localization. For step 2, the deep learning model uses a U-net architecture to perform voxel-wise correction to the 2D-3D DVF solved by step 1, especially at the caudal liver surface region where limited tissue contrast diminishes the accuracy of 2D-3D registration. The population-based model was trained on a total of 21 patients through supervised learning, using patient-specific 2D-3D DVF and liver contour pairs as input, and high-accuracy liver surface DVF as ‘gold-standard’ output. The ‘gold-standard’ DVF was obtained by manually contouring, density-overriding, and deform-registering liver organs on corresponding CT-CBCT pairs. We evaluated the technique on 10 liver patients not included in the training list. Each patient had a contrast-enhanced 4D-CT set, of which the 0% phase was used as the planning CT, and phases 10-90% as 4D-CBCTs to simulate 4D cone-beam projections for tumor localization evaluation. Liver tumors were contoured by a radiation oncologist on all 4D-CT phases for propagation (phase 0%) or reference (phases 10-90%). Both DICE similarity index and center-of-mass-error (COME) were calculated between propagated liver tumor contours and physician’s reference contours for quantitative analysis. Using 20 cone-beam projections for each localization, the 2D-3D registration technique, biomechanical modeling technique (without deep learning), and our technique localized the liver tumors to average ± s.d. COMEs of 4.5 ± 1.4 mm, 2.5 ± 0.8 mm, and 1.6 ± 0.5 mm, and DICEs of 0.61 ± 0.23, 0.73 ± 0.16, and 0.80 ± 0.09, respectively. The developed technique enables automatic, accurate liver tumor localization (<2 mm) using few projections (< = 20), which facilitates 4D tumor localization after projection phase sorting.

Liver Tumor Localization Using an Integrated Hybrid Fuzzy Seed Point Region Growing Algorithm

Liver Tumor Localization Using an Integrated Hybrid Fuzzy Seed Point Region Growing Algorithm

Liver Tumor Localization Using an Integrated Hybrid Fuzzy Seed Point Region Growing Algorithm

Liver Tumor Localization Using an Integrated Hybrid Fuzzy Seed Point Region Growing Algorithm

Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.

Pre-treatment liver tumor localization remains a challenging task for radiation therapy, mostly due to the limited tumor contrast against normal liver tissues, and the respiration-induced liver tumor motion. Recently, we developed a biomechanical modeling-based, deformation-driven cone-beam CT estimation technique (Bio-CBCT), which achieved substantially improved accuracy on low-contrast liver tumor localization. However, the accuracy of Bio-CBCT is still affected by the limited tissue contrast around the caudal liver boundary, which reduces the accuracy of the boundary condition that is fed into the biomechanical modeling process. In this study, we developed a motion modeling and biomechanical modeling-guided CBCT estimation technique (MM-Bio-CBCT), to further improve the liver tumor localization accuracy by incorporating a motion model into the CBCT estimation process. MM-Bio-CBCT estimates new CBCT images through deforming a prior high-quality CT or CBCT volume. The deformation vector field (DVF) is solved by iteratively matching the digitally-reconstructed-radiographs (DRRs) of the deformed prior image to the acquired 2D cone-beam projections. Using the same solved DVF, the liver tumor volume contoured on the prior image can be transferred onto the new CBCT image for automatic tumor localization. To maximize the accuracy of the solved DVF, MM-Bio-CBCT employs two strategies for additional DVF optimization: (I) prior-knowledge-guided liver boundary motion modeling with motion patterns extracted from a prior 4D imaging set like 4D-CTs/4D-CBCTs, to improve the liver boundary DVF accuracy; and (II) finite-element-analysis-based biomechanical modeling of the liver volume to improve the intra-liver DVF accuracy. We evaluated the accuracy of MM-Bio-CBCT on both the digital extended-cardiac-torso (XCAT) phantom images and real liver patient images. The liver tumor localization accuracy of MM-Bio-CBCT was evaluated and compared with that of the purely intensity-driven 2D-3D deformation technique, the 2D-3D deformation technique with motion modeling, and the Bio-CBCT technique. Metrics including the DICE coefficient and the center-of-mass-error (COME) were assessed for quantitative evaluation. Using limited-view 20 projections for CBCT estimation, the average (± SD) DICE coefficients between the estimated and the 'gold-standard' liver tumors of the XCAT study were 0.57±0.31, 0.78±0.26, 0.83±0.21, and 0.89±0.11 for 2D-3D deformation, 2D-3D deformation with motion modeling, Bio-CBCT and MM-Bio-CBCT techniques, respectively. Using 20 projections for estimation, the patient study yielded average DICE results of 0.63±0.21, 0.73±0.13 and 0.78±0.12, and 0.83±0.09, correspondingly. The MM-Bio-CBCT localized the liver tumor to an average COME of ~2 mm for both the XCAT and the liver patient studies. Compared to Bio-CBCT, MM-Bio-CBCT further improves the accuracy of liver tumor localization. MM-Bio-CBCT can potentially be used towards pre-treatment liver tumor localization and intra-treatment liver tumor location verification to achieve substantial radiotherapy margin reduction.

4D liver tumor localization using cone-beam projections and a biomechanical model

PurposeTo improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/MaterialsTo solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step’s accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling.We evaluated Bio-CBCT-est’s accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT ‘reference’ images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. ResultsUsing 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from −0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. ConclusionsBio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

CAHECA: computer aided hepatocellular carcinoma therapy planning.

Hepatocellular Carcinoma is the most common type of liver cancer having a strong relation with cirrhosis. Undoubtedly, cirrhosis may be caused by the virus infection of hepatitis B (HBV) and hepatitis C (HBC) or through alchoholism. However, even when cirrhosis has not been developed, patients with hepatitis viral infections are still at the risk of liver cancer. Apparently, among the numerous medical imaging techniques, Computed Tomography (CT) is the best in defining liver tumor borders. Unfortunately, these imaging techniques, including the CT procedures, usually rely on an appended application to reconstruct the generated 2-D slices to 3-D model. This may involve high performance computation, may be time-consuming or costly. Moreover, even with the outstanding performances of CT in defining the liver tumor boundaries, contrast between tumor tissues and the surrounding liver parenchyma is too low in CT slices. With such a close proxity in the tumor and the surrounding liver tissues, accurate characterization of liver tumor is a challenge. Previously, algorithms were developed to reveal abnormalities in brain's MRI datasets and CT abdominal pelvic, however, introducing a framework that could accurately characterize liver tumor and its surrounding tissues in CT datasets would go a long way in contributing to medical diagnosis and therapy planning of Hepatocellular Carcinoma. This paper proposes an Hepatocellular Carcinoma framework by extending the functionalities of SurLens Visualization System with an automatic liver tumor localization technique using Compute Unified Device Architecture (CUDA). The study was evaluated with liver CT datasets from the Imaging Science and Information Systems (ISIS) Center, the Georgetown University Medical Center. Significantly, visualization of liver CT datasets and the localization of the entangled tumor was achieved without prior datasets segmentation. Interestingly, the framework achieved remarkably good processing speed at a reasonably cheaper cost with an immediate reconstruction of the datasets and mapping of the tumor tissues within the surrounding liver parenchyma.

Sonographic demonstration of Couinaud's liver segments.

The segmental localization of liver tumors is critical to planning appropriate resection. Couinaud's nomenclature is a surgically relevant system of hepatic segmental anatomy, which defines the liver segments by their relationships to vascular structures, hepatic ligaments, and the gallbladder. We demonstrate a way to accurately localize hepatic masses with sonography.