Liver Segments Research Articles

Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields

Liver segmentation from CT images is a fundamental and essential step for many clinical applications, such as disease diagnosis, surgery navigator, and radiation therapy. Due to the limited computing resources, automatic and accurate liver segmentation from 3D CT volume is still a challenge task. In this paper, we propose an automatic liver segmentation method by fusing multi-view information. First, a 2D network combined with Dual Self-Attention (DSA) is proposed and applied on multi-sectional 2D slices reconstructed from axial, coronal, and sagittal directions. Then, to generate 3D segmentation results, a lightweight 3D network is designed to fuse the segmentation results from different viewing. Finally, a full connection condition random field is used to refine the 3D results. The proposed method is evaluated on four public datasets, including 3DIRCADb, CHAOS, Sliver07, and LiTS. The experimental results show that the proposed method can accurately segment the liver region for the datasets with large or small cases, as well as healthy or lesion livers. The dice values obtained by the proposed method on four datasets are 0.952, 0.969, 0.958, and 0.964, respectively, which are better than those obtained by many existing ones.

Comprehensive deep learning-based assessment of living liver donor CT angiography: from vascular segmentation to volumetric analysis.

Precise preoperative assessment of liver vasculature and volume in living donor liver transplantation is essential for donor safety and recipient surgery. Traditional manual segmentation methods are being supplemented by deep learning (DL) models, which may offer more consistent and efficient volumetric evaluations. This study analyzed living liver donors from Samsung Medical Center using preoperative CT angiography data between April 2022 and February 2023. A DL-based 3D residual U-Net model was developed and trained on segmented CT images to calculate the liver volume and segment vasculature, with its performance compared to traditional manual segmentation by surgeons and actual graft weight. The DL model achieved high concordance with manual methods, exhibiting Dice Similarity Coefficients of 0.94±0.01 for the right lobe and 0.91±0.02 for the left lobe. The liver volume estimates by DL model closely matched those of surgeons, with a mean discrepancy of 9.18mL, and correlated more strongly with actual graft weights (R-squared value of 0.76 compared to 0.68 for surgeons). The DL model demonstrates potential as a reliable tool for enhancing preoperative planning in liver transplantation, offering consistency and efficiency in volumetric assessment. Further validation is required to establish its generalizability across various clinical settings and imaging protocols.

Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML).

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model's parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively.

A Novel Liver Tumor segmentation of Adverse Propagation Advanced Swin Transformer Network with Mask region-based convolutional neural networks

The diagnosis and treatment of liver diseases from computed tomography (CT) images is an indispensable task for segmentation of Liver & its tumours. Due to the uneven presence, fuzzy borders, diverse densities, shapes and sizes of lesions segmentation of liver & its tumour is a difficult task. At this point we mainly focused on deep learning algorithms for segmenting liver and its tumour from abdominal CT scan images thereafter minimising the time & energy used for a liver diseases diagnosis This study aims to classify and segment liver tumors using a novel deep learning-based model. A Mask region-based convolutional neural network (Mask R-CNN) model is proposed for multiorgan segmentation to aid esophageal radiation treatment. Due to the fact that organ boundaries may be fuzzy and organ shapes are various, original Mask R-CNN works well on natural image segmentation while leaves something to be desired on the multiorgan segmentation task. Addressing it, the advantages of this method are threefold: (1) a ROI (region of interest) generation method is presented in the RPN (region proposal network) which is able to utilize multiscale semantic features. (2) A prebackground classification subnetwork is integrated to the original mask generation branch to improve the precision of multiorgan segmentation. The segmented image is added to the Adverse Propagation Advanced Swin Transformer Network (APESTNet) to prevent overfitting. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01.The proposed model dataset is split into training as 60% and testing as 40 % for classification. The proposed model's accuracy and recall are 99.23 % and 98.24 %, respectively.

CoProLITE: Constrained Proxy Learning for lIver and hepaTic lesion sEgmentation

Liver and hepatic lesion segmentation is an important task in medical image analysis, which plays a crucial role in diagnosis, treatment planning and monitoring of liver diseases. We observed an ordinal layout of the feature space that aligns with CT image characteristics will improve performance on liver and hepatic lesion segmentation task. In order to enforce the samples to conform to a specific layout of the feature space, we propose a novel liver and hepatic lesion segmentation method called CoProLITE, which learns a constrained proxy for each classes. Specifically, We replace the traditional FCN-based segmentation head by a proxy learning-based head to learn feature representations of the images, and introduces constraints during the training process to guide the learning of the proxies. We extensively evaluate CoProLITE on three public datasets and compare it to state-of-the-art methods. The experimental results demonstrate the effectiveness of the proposed method.

Resección hepática en enfermedad de Caroli unilobar

Caroli’s disease is a congenital disorder caused by a defect of the ductal plate. The clinical picture includes intrahepatic duct lithiasis and recurrent cholangitis usually in young people. The imaging tests reveal the presence of multifocal and segmental dilatation of the intrahepatic bile ducts. Cholangiocarcinoma can develop as a long-term complication of chronic bile duct inflammation and should always be considered as a differential diagnosis. We describe a rare case of a 52-year-old woman who presented with bile duct obstruction after laparoscopic cholecystectomy and required resection of liver segments II and III due to Caroli’s disease with a favorable outcome.

Automated segmentation of liver tumors from computed tomographic scans

The precision of liver tumor segmentation heavily depends on the doctor's expertise, hence it is required to produce an algorithm for automatic liver tumor segmentation to reduce the manual intervention in assessing liver disease identification. We propose a CNN-based UNet architecture designed to segment liver tumors from CT images of size 128×128. In this model, modifications were made to the encoder, decoder, and bridge paths to enhance feature extraction efficiency. The performance of the modified UNet was evaluated against an existing segmentation method using the same CT image size. The comparison focused on the Dice similarity coefficient and accuracy. Our proposed method demonstrated a high Dice similarity coefficient of 75.37 % and an accuracy of 99.75 % on the 3Dircadb dataset. These results indicate that our modified UNet achieved superior segmentation metrics compared to state-of-the-art methods, showcasing its effectiveness in liver tumor segmentation.

Applying LASSO logistic regression for the prediction of biliary complications after ex vivo liver resection and autotransplantation in patients with end-stage hepatic alveolar echinococcosis

BackgroundThe purpose of this study was to explore the relevant risk factors associated with biliary complications (BCs) in patients with end-stage hepatic alveolar echinococcosis (HAE) following ex vivo liver resection and autotransplantation (ELRA) and to establish and visualize a nomogram model.MethodsThis study retrospectively analysed patients with end-stage HAE who received ELRA treatment at the First Affiliated Hospital of Xinjiang Medical University between August 1, 2010 and May 10, 2023. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize the feature variables for predicting the incidence of BCs following ELRA. Multivariate logistic regression analysis was used to develop a prognostic model by incorporating the selected feature variables from the LASSO regression model. The predictive ability, discrimination, consistency with the actual risk, and clinical utility of the candidate prediction model were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed by the bootstrapping method.ResultsThe candidate prediction nomogram included predictors such as age, hepatic bile duct dilation, portal hypertension, and regular resection based on hepatic segments. The model demonstrated good discrimination ability and a satisfactory calibration curve, with an area under the ROC curve (AUC) of 0.818 (95% CI 0.7417–0.8958). According to DCA, this prediction model can predict the risk of BCs occurrence within a probability threshold range of 9% to 85% to achieve clinical net benefit.ConclusionsA prognostic nomogram with good discriminative ability and high accuracy was developed and validated to predict BCs after ELRA in patients with end-stage HAE.

A pilot study of virtual liver segment projection technology in subsegment-oriented laparoscopic anatomical liver resection when indocyanine green staining fails (with video).

Precision surgery for liver tumors favors laparoscopic anatomical liver resection (LALR), involving the removal of specific liver segments or subsegments. Indocyanine green (ICG)-negative staining is a commonly used method for defining resection boundaries but may be prone to failure. The challenge arises when ICG staining fails, as it cannot be repeated during surgery. In this study, we employed the virtual liver segment projection (VLSP) technology as a salvage approach for precise boundary determination. Our aim was to assess the feasibility of the VLSP to be used for the determination of the boundaries of the liver resection in this situation. Between January 2021 and June 2023, 12 consecutive patients undergoing subsegment-oriented LALR were included in this pilot series. The VLSP technology was utilized to define the resection boundaries at the time of ICG-negative staining failure. Routine surgical parameters and short-term outcomes were evaluated to assess the safety of VLSP in this procedure. In addition, its feasibility was assessed by analyzing the accuracy between the predicted resected liver volume (PRLV) and actual resected liver volume (ARLV). Of the 12 enrolled patients, the mean operation time was 444.58 ± 101.70min (range 290-570min), with a mean blood loss of 125.00 ± 96.53ml (range 50-400mL). One patient (8.3%) was converted to laparotomy for subsequent parenchymal transection, four (33.3%) received blood transfusions and four (33.3%) had postoperative complications. All patients received an R0 resection. The Pearson correlation coefficient (r) between PRLV and ARLV was 0.98 (R2 = 0.96, p < 0.05), and the relative error (RE) was 8.62 ± 6.66% in the 12 patients, indicating agreement. Failure of intraoperative ICG-negative staining during subsegment-oriented LALR is possible, and VLSP may be an alternative to define the resection boundaries in such cases.

Laparoscopic left hemihepatectomy using augmented reality navigation plus ICG fluorescence imaging for hepatolithiasis: a retrospective single-arm cohort study (with video).

Laparoscopic left hemihepatectomy (LLH) has been shown to be an effective and safe method for treating hepatolithiasis primarily affecting the left hemiliver. However, this procedure still presents challenges. Due to pathological changes in intrahepatic duct stones, safely dissecting the hilar vessels and determining precise resection boundaries remains difficult, even with fluorescent imaging. Our team proposed a new method of augmented reality navigation (ARN) combined with Indocyanine green (ICG) fluorescence imaging for LLH in hepatolithiasis cases. This study aimed to investigate the feasibility of this combined approach in the procedure. Between May 2021 and September 2023, 16 patients with hepatolithiasis who underwent LLH were included. All patients underwent preoperative 3D evaluation and were then guided using ARN and ICG fluorescence imaging during the procedure. Perioperative and short-term postoperative outcomes were assessed to evaluate the safety and efficacy of the method. All 16 patients successfully underwent LLH. The mean operation time was 380.31 ± 92.17min, with a mean estimated blood loss of 116.25 ± 64.49ml. ARN successfully aided in guiding hilar vessel dissection in all patients. ICG fluorescence imaging successfully identified liver resection boundaries in 11 patients (68.8%). In the remaining 5 patients (31.3%) where fluorescence imaging failed, virtual liver segment projection (VLSP) successfully identified their resection boundaries. No major complications occurred in any patients. Immediate stone residual rate, stone recurrence rate, and stone extraction rate through the T-tube sinus tract were 12.5%, 6.3%, and 6.3%, respectively. The combination of ARN and ICG fluorescence imaging enhances the safety and precision of LLH for hepatolithiasis. Moreover, ARN may serve as a safe and effective tool for identifying precise resection boundaries in cases where ICG fluorescence imaging fails.

Skin Cancer Segmentation using CNN

This groundbreaking research introduces a comprehensive strategy for advancing medical image segmentation, merging two pivotal concepts to significantly enhance both the accuracy and efficiency of the segmentation process. The first component of our approach involves the integration of polar transformations as a preprocessing step applied to the original dataset. This transformative technique is designed to address the challenges associated with segmenting single structures of elliptical shape in medical images, such as organs (e.g., heart and kidneys), skin lesions, polyps, and various abnormalities. By centering the polar transformation on the object's focal point, a reduction in dimensionality is achieved, coupled with a distinct separation of segmentation and localization tasks. Two distinct methodologies for selecting an optimal polar origin are proposed: one involving estimation through a segmentation neural network trained on non-polar images, and the other employing a dedicated neural network trained to pre dict the optimal origin. The second key element of our approach is around the integration of the DoubleU-Net architecture, a powerful encoder-decoder model specifically designed for the task of semantic image segmentation. DoubleU- Net is a group of two U-Net architectures, each with a specific purpose. The initial U-Net is pre-trained on VGG-19 as the encoder and uses features learned from ImageNet to provide efficient information transfer. In order to store more semantic information and content, a second U-Net was added to the base to enhance the capabilities of the network. Join Atrous Spatial Pyramid Pooling (ASPP) to develop network data extraction content. The combination of DoubleU-Net architecture and joint transformation as a step forward shows good segmentation performance in different clinical tasks, including liver segmentation, polyp detection vision, skin segmentation, and epicardial fat tissue segmentation. It shows that various medical projects, including various diagnostic methods such as colonoscopy, dermoscopy, microscopy, have a positive impact on the plan. More importantly, the method performs well in difficult cases, such as the segmentation of small and flat polyps in CVC-ClinicDB and the 2015 subset of the MICCAI Automated Polyp Detection dataset. The results demonstrate the accuracy and generality of the combination, making it the best way to evaluate medical images in context; Our study has revealed a new method of skin cancer diagnosis that combines the power of deep learning with innovation. Advanced technology. The combination of dual U-Net architecture and polar coordinate transformation not only improves the accuracy of classification of lesions but also improves the robustness of the model to changes in image features. This study contributes to the development of computer-aided diagnostic systems for early diagnosis. Experimental results show that our method provides good accuracy, sensitivity, and specificity in detecting malignant and benign tumors. Additionally, we are conducting ablation studies to determine the contribution of each presentation and treatment of skin cancer to ultimately benefit patients and determine these benefits. We also apply the transformation of the joint as the first step to improve the discrimination ability of the model. This mechanical change effectively reduces the impact caused by changes in wound size, shape, and direction by displaying the original image of the polar system. By standardizing the representation of skin diseases, polar transformation improves the model's ability to generalize across different data sets and improves overall performance.

Clinical feasibility of laparoscopic left lateral segment liver resection with magnetic anchor technique: The first clinical study from China.

Magnetic anchor technique (MAT) has been applied in laparoscopic cholecystectomy and laparoscopic appendectomy, but has not been reported in laparoscopic partial hepatectomy. To evaluate the feasibility of the MAT in laparoscopic left lateral segment liver resection. Retrospective analysis was conducted on the clinical data of eight patients who underwent laparoscopic left lateral segment liver resection assisted by MAT in our department from July 2020 to November 2021. The Y-Z magnetic anchor devices (Y-Z MADs) was independently designed and developed by the author of this paper, which consists of the anchor magnet and magnetic grasping apparatus. Surgical time, intraoperative blood loss, intraoperative accidents, operator experience, postoperative incision pain score, postoperative complications, and other indicators were evaluated and analyzed. All eight patients underwent a MAT-assisted laparoscopic left lateral segment liver resection, including three patients undertaking conventional 5-port and five patients having a transumbilical single-port operation. The mean operation time was 138 ± 34.32 min (range 95-185 min) and the mean intraoperative blood loss was 123 ± 88.60 mL (range 20-300 mL). No adverse events occurred during the operation. The Y-Z MADs showed good workability and maneuverability in both tissue and organ exposure. In particular, the operators did not experience either a "chopstick" or "sword-fight" effect in the single-port laparoscopic operation. The results show that the MAT is safe and feasible for laparoscopic left lateral segment liver resection, especially, exhibits its unique abettance for transumbilical single-port laparoscopic left lateral segment liver resection.

Automatic liver segmentation using U-Net deep learning architecture for additive manufacturing

Automatic liver segmentation using U-Net deep learning architecture for additive manufacturing

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p<0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p<0.001). The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

Selective internal radiation therapy segmentectomy: A new minimally invasive curative option for primary liver malignancies?

Transarterial radioembolization or selective internal radiation therapy (SIRT) has emerged as a minimally invasive approach for the treatment of tumors. This percutaneous technique involves the local, intra-arterial delivery of radioactive microspheres directly into the tumor. Historically employed as a palliative measure for liver malignancies, SIRT has gained traction over the past decade as a potential curative option, mirroring the increasing role of radiation segmentectomy. The latest update of the BCLC hepatocellular carcinoma guidelines recognizes SIRT as an effective treatment modality comparable to other local ablative methods, particularly well-suited for patients where surgical resection or ablation is not feasible. Radiation segmentectomy is a more selective approach, aiming to deliver high-dose radiation to one to three specific hepatic segments, while minimizing damage to surrounding healthy tissue. Future research efforts in radiation segmentectomy should prioritize optimizing radiation dosimetry and refining the technique for super-selective administration of radiospheres within the designated hepatic segments.

Isolated liver chemoperfusion in a patient with mesenteric leiomyosarcoma and liver metastasis

Background. Treatment for inoperable secondary liver cancer remains challenging especially in patients with colorectal cancer. Traditional systemic drug therapy is often ineffective, and the use of transarterial chemoembolization is limited due to the systemic toxicity of some drugs. Combining high efficacy and low systemic toxicity, isolated chemoperfusion is a promising treatment option for patients with liver metastasis. Case report. A patient with mesentery leiomyosarcoma underwent several surgeries and chemotherapy courses, but liver metastases continued to grow. Isolated liver chemoperfusion with melphalan was performed after resection of liver segment and mobilization of liver vessels. To reduce the tumor volume and prevent complications, atypical liver resection was also performed. Conclusion. Isolated liver chemoperfusion for the treatment of liver cancer remains a subject of debate and is not included in the standards for treatment of primary and metastatic liver tumors. This technique was shown to be safe and promising in treating liver metastasis from leiomyosarcoma. However, further research is required to assess the role of this procedure in overall survival.

Federated 3D multi-organ segmentation with partially labeled and unlabeled data.

This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsistent, partial annotations across different institutes. Learning a federated model from these distributed, partially labeled, and unlabeled samples is an unexplored problem. To simulate this multi-organ segmentation problem, several distributed clients and a central server are maintained. The central server coordinates with clients to learn a global model using distributed private datasets, which comprise a small part of partially labeled images and a large part of unlabeled images. To address this problem, a practical framework that unifies partially supervised learning (PSL), semi-supervised learning (SSL), and federated learning (FL) paradigms with PSL, SSL, and FL modules is proposed. The PSL module manages to learn from partially labeled samples. The SSL module extracts valuable information from unlabeled data. Besides, the FL module aggregates local information from distributed clients to generate a global statistical model. With the collaboration of three modules, the presented scheme could take advantage of these distributed imperfect datasets to train a generalizable model. The proposed method was extensively evaluated with multiple abdominal CT datasets, achieving an average result of 84.83% in Dice and 41.62mm in 95HD for multi-organ (liver, spleen, and stomach) segmentation. Moreover, its efficacy in transfer learning further demonstrated its good generalization ability for downstream segmentation tasks. This study considers a novel problem of multi-organ segmentation, which aims to develop a generalizable model using distributed, partially labeled, and unlabeled CT images. A practical framework is presented, which, through extensive validation, has proved to be an effective solution, demonstrating strong potential in addressing this challenging problem.

AD-DUNet: A dual-branch encoder approach by combining axial Transformer with cascaded dilated convolutions for liver and hepatic tumor segmentation

Liver cancer remains a significant health concern, and accurate segmentation in CT scans is crucial for diagnosis and treatment. Deep learning-based auxiliary diagnosis techniques, especially utilizing U-shaped structures, are widely employed in medical image segmentation. However, traditional methods that utilize Convolutional Neural Networks (CNNs) generally have limitations in modeling long-range dependencies. Inspired by the success of Transformers in various vision tasks, approaches that combine Transformers with CNNs have been spurred. However, many existing hybrid CNN-Transformer models are prone to yielding poor performance on relative small-scale medical image datasets when trained from scratch. Moreover, some of these methods involve additional fusion modules customized, which introduce extra workload and parameters to the model. To address these limitations, we propose AD-DUNet, a hybrid CNN-Transformer model for liver and hepatic tumor segmentation, which comprises a dual-branch encoder and a residual decoder. The Transformer-based encoder, utilizing Axial Transformer (AT) blocks, efficiently captures long-range dependencies across the entire image, while the CNN-based encoder, constructed with cascaded dilated convolutions (CDC) blocks, extracts fine-grained local features. The two encoders synergize in the shared residual decoder, eliminating the need for additional fusion modules. The extensive experiments conducted on the LiTS2017 and 3DIRCAD datasets demonstrate the superiority of AD-DUNet over existing models. Remarkably, our approach achieves state-of-the-art results without relying on pre-trained weights, showcasing its efficiency with low complexity and 4.24M parameters.

Characterization of Microwave Generator Energy and Ablation Volumes following Transarterial Embolization in an In Vivo Porcine Liver Model

PurposeTo characterize the relationship between ablation zone volume (AZV) and microwave ablation (MWA) energy in an in vivo porcine liver model following arterial embolization. Materials and MethodsWith Institutional Animal Care and Use Committee (IACUC) approval, 11 female swine underwent either right (n = 5) or left (n = 6) hepatic artery embolization under fluoroscopic guidance. Subsequently, ultrasound (US)-guided MWA was performed in each liver segment (left lateral, left medial, right medial, and right lateral) at either 30 W (n = 4 lobes), 60 W (n = 4), 65 W (n = 20), 90 W (n = 8), 120 W (n = 4), or 140 W (n = 4) continuously for 5 minutes. Postprocedural volumetric segmentation was performed on standardized multiphase T1 magnetic resonance (MR) imaging sequences. ResultsMean AZVs in embolized lobes (15.8 mL ± SD 10.6) were significantly larger than those in nonembolized lobes (11.2 mL ± SD 6.5, P < .01). MWA energy demonstrated significant positive linear correlation with both embolized (R2 = 0.66, P < .01) and nonembolized (R2 = 0.64, P < .01) lobes. The slope of the linear models corresponded to a 0.95 mL/kJ (SD ± 0.16) and 0.54 mL/kJ (SD ± 0.09) increase in ablation volume per applied kilojoule of energy (E) in embolized and nonembolized lobes, respectively. In the multivariate model, embolization status significantly modified the relationship between E and AZV as described by the following interaction term: 0.42∗E∗(embolization status) (P = .031). ConclusionsLinear models demonstrated a near 1.8-fold increase in ratio of AZV per unit E, R(AZV:E), when applied to embolized lobes relative to nonembolized lobes. Absolute AZV differences between embolized and nonembolized lobes were greater at higher-power MWA.

Encysted Odyssey: A Clinical and Pictorial Analysis of Hydatid Cysts From Head to Toe.

Cystic echinococcosis, a zoonotic disease caused by the larval form of Echinococcus granulosus, predominantly affects the liver and lungs, with humans acting as accidental hosts. Our retrospective study at the Department of Radiology and Imageology, Nizam's Institute of Medical Sciences, included 187 histopathologically or serologically proven cases. The mean age of presentation was 49.4 years. Liver involvement was most prevalent, accounting for 83.4% (n=156) of cases, followed by sporadic involvement of other organs such as the mesentery, spleen, pancreas, thalamus, kidney, lung, spine, and omentum. Characteristic diagnostic features observed on imaging included peripheral calcifications in 33% of cases, internal septations in 25% (n=47), dense calcifications in 15% (n=29), daughter cysts in 6% (n=11), and floating membranes in 5% (n=10). Among hepatic lesions, 90% (n=141) were showing involvement of a single lobe. Notably, 78% (n=110) of lesions were limited to the right lobe, 21% (n=30) to the left lobe, and 1% (n=1) to the caudate lobe. The most affected hepatic segment was segment VIII, while the least common was segment I (caudate lobe). Complications were identified in 13% (n=25) of cases of hepatic hydatidosis. The findings of our study emphasize the systemic nature of E. granulosusinfection which can affect various organs in the body. It also illustrates the invaluable insights imaging provides for timely and accurate diagnosis of hydatid disease.