SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation.

The real-time requirement for image segmentation in laparoscopic surgical assistance systems is extremely high. Although traditional deep learning models can ensure high segmentation accuracy, they suffer from a large computational burden. In the practical setting of most hospitals, where powerful computing resources are lacking, these models cannot meet the real-time computational demands. We propose a novel network SwinD-Net based on Skip connections, incorporating Depthwise separable convolutions and Swin Transformer Blocks. To reduce computational overhead, we eliminate the skip connection in the first layer and reduce the number of channels in shallow feature maps. Additionally, we introduce Swin Transformer Blocks, which have a larger computational and parameter footprint, to extract global information and capture high-level semantic features. Through these modifications, our network achieves desirable performance while maintaining a lightweight design. We conduct experiments on the CholecSeg8k dataset to validate the effectiveness of our approach. Compared to other models, our approach achieves high accuracy while significantly reducing computational and parameter overhead. Specifically, our model requires only 98.82 M floating-point operations (FLOPs) and 0.52 M parameters, with an inference time of 47.49 ms per image on a CPU. Compared to the recently proposed lightweight segmentation network UNeXt, our model not only outperforms it in terms of the Dice metric but also has only 1/3 of the parameters and 1/22 of the FLOPs. In addition, our model achieves a 2.4 times faster inference speed than UNeXt, demonstrating comprehensive improvements in both accuracy and speed. Our model effectively reduces parameter count and computational complexity, improving the inference speed while maintaining comparable accuracy. The source code will be available at https://github.com/ouyangshuiming/SwinDNet.

Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization.

RATIONALE AND OBJECTIVES: The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE). MATERIALS AND METHODS: This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets. RESULTS: A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation. CONCLUSION: Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.

MT-FiST: A Multi-Task Fine-Grained Spatial-Temporal Framework for Surgical Action Triplet Recognition.

Surgical action triplet recognition plays a significant role in helping surgeons facilitate scene analysis and decision-making in computer-assisted surgeries. Compared to traditional context-aware tasks such as phase recognition, surgical action triplets, comprising the instrument, verb, and target, can offer more comprehensive and detailed information. However, current triplet recognition methods fall short in distinguishing the fine-grained subclasses and disregard temporal correlation in action triplets. In this article, we propose a multi-task fine-grained spatial-temporal framework for surgical action triplet recognition named MT-FiST. The proposed method utilizes a multi-label mutual channel loss, which consists of diversity and discriminative components. This loss function decouples global task features into class-aligned features, enabling the learning of more local details from the surgical scene. The proposed framework utilizes partial shared-parameters LSTM units to capture temporal correlations between adjacent frames. We conducted experiments on the CholecT50 dataset proposed in the MICCAI 2021 Surgical Action Triplet Recognition Challenge. Our framework is evaluated on the private test set of the challenge to ensure fair comparisons. Our model apparently outperformed state-of-the-art models in instrument, verb, target, and action triplet recognition tasks, with mAPs of 82.1% (+4.6%), 51.5% (+4.0%), 45.50% (+7.8%), and 35.8% (+3.1%), respectively. The proposed MT-FiST boosts the recognition of surgical action triplets in a context-aware surgical assistant system, further solving multi-task recognition by effective temporal aggregation and fine-grained features.

UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network.

Background: Methods based on the combination of transformer and convolutional neural networks (CNNs) have achieved impressive results in the field of medical image segmentation. However, most of the recently proposed combination segmentation approaches simply treat transformers as auxiliary modules which help to extract long-range information and encode global context into convolutional representations, and there is a lack of investigation on how to optimally combine self-attention with convolution. Methods: We designed a novel transformer block (MRFormer) that combines a multi-head self-attention layer and a residual depthwise convolutional block as the basic unit to deeply integrate both long-range and local spatial information. The MRFormer block was embedded between the encoder and decoder in U-Net at the last two layers. This framework (UMRFormer-Net) was applied to the segmentation of three-dimensional (3D) pancreas, and its ability to effectively capture the characteristic contextual information of the pancreas and surrounding tissues was investigated. Results: Experimental results show that the proposed UMRFormer-Net achieved accuracy in pancreas segmentation that was comparable or superior to that of existing state-of-the-art 3D methods in both the Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) dataset and the public Medical Segmentation Decathlon dataset (self-division). UMRFormer-Net statistically significantly outperformed existing transformer-related methods and state-of-the-art 3D methods (P<0.05, P<0.01, or P<0.001), with a higher Dice coefficient (85.54% and 77.36%, respectively) or a lower 95% Hausdorff distance (4.05 and 8.34 mm, respectively). Conclusions: UMRFormer-Net can obtain more matched and accurate segmentation boundary and region information in pancreas segmentation, thus improving the accuracy of pancreas segmentation. The code is available at https://github.com/supersunshinefk/UMRFormer-Net.

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.

BACKGROUND: The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training. PURPOSE: The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-based methods. METHODS: We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imaging results. RESULTS: The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel, respectively. CONCLUSIONS: The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurate registration.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Diagnostic accuracy of 3D imaging combined with intra-operative ultrasound in the prediction of post-hepatectomy liver failure.

Background: The risk of post-hepatectomy liver failure (PHLF) is difficult to predict preoperatively. Accurate preoperative assessment of residual liver volume is critical in PHLF. Three-dimensional (3D) imaging and intra-operative ultrasound (IOUS) offer significant advantages in calculating liver volume and have been widely used in hepatectomy risk assessment. Our research aimed to explore the accuracy of 3D imaging technique combining IOUS in predicting PHLF after hepatectomy. Methods: We used a retrospective study design to analyze patients who underwent hepatectomy with 3D imaging combined with IOUS between 2017 and 2020. Utilizing 3D reconstruction, the patient's residual liver volumes (PRLVs) and ratio of PRLV to standard liver volume (SLV) were calculated preoperatively. Hepatectomy were performed and actual hepatectomy volume (AHV) were measured. Consistency between preoperative planned hepatectomy volume (PPHV) and AHV was quantified postoperatively by Bland-Altman analysis. Multiple logistic regression and receiver-operating characteristic (ROC) curves were utilized to discuss the predictive value of PRLV/SLV in PHLF. Results: Among the 214 included patients, 58 (27.1%) had PHLF. Patients with PHLF had significantly higher residual rates of ICG-R15 (%) (P=0.000) and a lower PRLV/SLV ratio (P=0.000). Bland-Altman analysis showed that PPHV was consistent with AHV (P=0.301). Multivariate analysis confirmed that PRLV/SLV ratio >60% (OR, 0.178; 95% CI: 0.084-0.378; P<0.01) was a protective factor for PHLF. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.8% (95% CI: 64.5.3-87.2%), 66.6% (95% CI: 59.1-74.1%), 45.8%, and 88.1%, respectively. The area under the ROC curve (AUC) was 73.7% (95% CI: 65.7-85.8%) and the diagnostic accuracy of PRLV/SLV for PHLF was moderate (P<0.001). These results were validated in the validation cohort perfectly. The primary cohort included 214 patients with a PHLF rate of 27.1% (n=58, 28 grade B and 13 grade C). The validation cohort included 135 patients with a PHLF rate of 35.6% (n=48, 24 grade B and 11 grade C). Conclusions: The calculation of PRLV/SLV has predictive value in PHLF and can be exploited as a predictive factor. The 3D imaging technique combined with IOUS may be useful for PHLF risk assessment in hepatectomy patients.

Acute abdominal disease associated with gallbladder torsion recovered after cholecystectomy: a rare case report and literature review.

Background: Gallbladder torsion is very rare and easily misdiagnosed as biliary disease. It is defined as the rotation of the gallbladder along the axis of the cystic pedicle on the mesentery. As gallbladder rotation involves the gallbladder artery, the blood supply is blocked, resulting in gallbladder ischemia and eventual necrosis. If misdiagnosis occurs and treatment is delayed, gallbladder torsion can develop into a lethal disease. The typical imaging features of gallbladder torsion in this case are a good learning resource for our young physicians, as well as providing a rare, unusual and typical case for our current literature database. Case Description: We present a rare case of gallbladder torsion in a 19-year-old man. The patient complained of sudden recurrent pain and discomfort in the right upper abdomen with vomiting for 12 hours. Abdominal ultrasound and computed tomography (CT) scan showed gallbladder enlargement and signs of acute cholecystitis in emergency examination, and there were no signs of cholecystolithiasis. Considering that the patient was a young male and the patients prefer conservative treatment, symptomatic treatment was given. However, there was no obvious effect after 1 day of medical treatment, but severe abdominal pain in the upper right quadrant continues to progress. Finally, the patient underwent laparoscopic cholecystectomy, and the gallbladder was found to be enlarged with ischemic necrosis, which was caused by gallbladder torsion. The patient recovered 2 days after surgery and was discharged without complications. Conclusions: Although the clinical manifestation is similar to that of typical acute calculous cholecystitis, gallbladder torsion can be diagnosed early through some special signs on imaging examination, such as distorted cystic duct signs ("beak and whirl" sign), gallbladder dilatation with gallbladder fossa effusion, and gallbladder in the horizontal position. These signs can help primary surgical treatment and prevent fatal complications such as gallbladder gangrene, perforation, and biliary peritonitis. Therefore, for inexperienced doctors, careful imaging features are required for the correct diagnosis of rare gallbladder torsion. Keywords: Gallbladder torsion; acute abdominal disease; cholecystitis; case report.

A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets.

BACKGROUND: Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions, different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study. METHODS: The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3) One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder. RESULTS: Model trained on different datasets has different segmentation performance. The prediction accuracy between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets. CONCLUSIONS: (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.

Morphologic Change of In Vivo Porcine Liver Under 13 mm Hg Pneumoperitoneum Pressure.

BACKGROUND: Clinically, the total and residual liver volume must be accurately calculated before major hepatectomy. However, liver volume might be influenced by pneumoperitoneum during surgery. Changes in liver volume change also affect the accuracy of simulation and augmented reality navigation systems, which are commonly first validated in animal models. In this study, the morphologic changes in porcine livers in vivo under 13 mm Hg pneumoperitoneum pressure were investigated. MATERIALS AND METHODS: Twenty male pigs were scanned with contrast-enhanced computed tomography without pneumoperitoneum and with 13 mm Hg pneumoperitoneum pressure. RESULTS: The surface area and volume of the liver and the vascular diameter of the aortic lumen, inferior vena cava lumen, and portal vein lumen were measured. There were statistically significant differences in the surface area and volume of the liver (P=0.000), transverse diameter of the portal vein (P=0.038), longitudinal diameter of the inferior vena cava (P=0.033), longitudinal diameter of the portal vein (P=0.036), vascular cross-sectional area of the inferior vena cava (P=0.028), and portal vein (P=0.038) before and after 13 mm Hg pneumoperitoneum pressure. CONCLUSIONS: This study indicated that the creation of pneumoperitoneum at 13 mm Hg pressure in a porcine causes liver morphologic alterations affecting the area and volume, as well as the diameter of a blood vessel.

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer.

PURPOSE: To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. METHODS: A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. RESULTS: The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835-0.930) in the training dataset and 0.846 (95% CI 0.757-0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730-0.987). CONCLUSION: A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

Outcomes of reoperation for hepatolithiasis with 3D imaging individualized preoperative planning technique: a single-center experience.

BACKGROUND: A detailed assessment of biliary tract anatomy is necessary for the successful reoperation for hepatolithiasis. This study aimed to evaluate the feasibility of preoperative individualized surgical planning with three-dimensional (3D) imaging technique for reoperation of hepatolithiasis. METHODS: This was a retrospective matched case-control study. From January 2011 to December 2018, 56 patients receiving reoperation according to the individualized preoperative plan based on 3D imaging at our center were included (group A). Meanwhile, 54 patients receiving traditional imaging guided reoperation matched by age, gender and distribution of hepatobiliary stones to each case were selected as controls (group B). The perioperative and long-term follow-up outcomes were compared between the two groups. RESULTS: There was no significant difference in demographic characteristics between groups. Compared with group B, the group A had a significantly shorter operation time (245.7±56.2 min vs. 305.2±79.9 min, P<0.001), a significantly higher surgical plan implementation rate (SPIR, 92.9% vs. 66.7%, P=0.001) and a lower incidence-of severe complications (Clavien-Dindo grade>II, 1.8% vs. 14.8%, P=0.015). The incidences of initial residual stone (7.1% vs. 44.4%, P<0.001) and repeated cholangitis (3.6% vs. 33.3%, P<0.001) were significantly lower in group A than in group B. After postoperative choledochoscopic lithotripsy, the incidence of final residual stones was significantly lower in group A than in group B. (1.8% vs. 20.4%, P=0.002). CONCLUSIONS: The preoperative 3D imaging assisted surgical planning is feasible and safe for reoperation of hepatolithiasis which can effectively improve surgical plan implementation rate and reduce the incidence of postoperative complications as compared with conventional surgical planning.

MDCC-Net: Multiscale double-channel convolution U-Net framework for colorectal tumor segmentation.

PURPOSE: Multiscale feature fusion is a feasible method to improve tumor segmentation accuracy. However, current multiscale networks have two common problems: 1. Some networks only allow feature fusion between encoders and decoders of the same scale. It is obvious that such feature fusion is not sufficient. 2. Some networks have too many dense skip connections and too much nesting between the coding layer and the decoding layer, which causes some features to be lost and means that not enough information will be learned from multiple scales. To overcome these two problems, we propose a multiscale double-channel convolution U-Net (MDCC-Net) framework for colorectal tumor segmentation. METHODS: In the coding layer, we designed a dual-channel separation and convolution module and then added residual connections to perform multiscale feature fusion on the input image and the feature map after dual-channel separation and convolution. By fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information. RESULTS: The segmentation results show that our proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 83.57%, which is an improvement of 9.59%, 6.42%, and 1.57% compared with nnU-Net, U-Net, and U-Net++, respectively. CONCLUSION: The experimental results show that our proposed method has good performance in the segmentation of colorectal tumors and is close to the expert level. The proposed method has potential clinical applicability.

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma.

OBJECTIVE: This study aimed to build and evaluate a radiomics feature-based model for the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma. METHODS: A total of 145 patients were retrospectively included in the study pool, and the patients were divided randomly into two independent cohorts with a ratio of 7:3 (training cohort: n = 101, validation cohort: n = 44). For a pilot study of this predictive model another 18 patients were recruited into this study. A total of 1,231 computed tomography (CT) image features of the liver parenchyma without tumors were extracted from portal-phase CT images. A least absolute shrinkage and selection operator (LASSO) logistic regression was applied to build a radiomics score (Rad-score) model. Afterwards, a nomogram, including Rad-score as well as other clinicopathological risk factors, was established with a multivariate logistic regression model. The discrimination efficacy, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS: The Rad-score scoring model could predict MVI with the area under the curve (AUC) of 0.637 (95% CI, 0.516-0.758) in the training cohort as well as of 0.583 (95% CI, 0.395-0.770) in the validation cohort; however, the aforementioned discriminative approach could not completely outperform those existing predictors (alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin). The individual predictive nomogram which included the Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin showed a better discrimination efficacy with AUC of 0.865 (95% CI, 0.786-0.944), which was higher than the conventional methods' AUCs (nomogram vs Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin at P < 0.001, P = 0.025, P < 0.001, and P = 0.001, respectively). When applied to the validation cohort, the nomogram discrimination efficacy was still outbalanced those above mentioned three remaining methods (AUC: 0.705; 95% CI, 0.537-0.874). The calibration curves of this proposed method showed a satisfying consistency in both cohorts. A prospective pilot analysis showed that the nomogram could predict MVI with an AUC of 0.844 (95% CI, 0.628-1.000). CONCLUSIONS: The radiomics feature-based predictive model improved the preoperative prediction of MVI in HCC patients significantly. It could be a potentially valuable clinical utility.

Augmented reality navigation for liver resection with a stereoscopic laparoscope.

OBJECTIVE: Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images. METHOD: In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels. RESULTS: The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ±â€¯1.85 mm and 8.73 ±â€¯2.43 mm, respectively. CONCLUSION AND SIGNIFICANCE: Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice.

A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma.

OBJECTIVES: To develop and validate a radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). METHODS: One hundred twelve consecutive HCC patients who underwent hepatectomy were included in the study pool (training cohort: n = 80, validation cohort: n = 32), and another 13 patients were included in a pilot prospective analysis. A total of 713 radiomics features were extracted from portal-phase computed tomography (CT) images. A logistic regression was used to construct a radiomics score (Rad-score). Then a nomogram, including Rad-score and other risk factors, was built with a multivariate logistic regression model. The discrimination, calibration and clinical utility of nomogram were evaluated. RESULTS: The Rad-score could predict PHLF with an AUC of 0.822 (95% CI, 0.726-0.917) in the training cohort and of 0.762 (95% CI, 0.576-0.948) in the validation cohort; however, the approach could not completely outmatch the existing methods (CP [Child-Pugh], MELD [Model of End Stage Liver Disease], ALBI [albumin-bilirubin]). The individual predictive nomogram that included the Rad-score, MELD and performance status (PS) showed better discrimination with an AUC of 0.864 (95% CI, 0.786-0.942), which was higher than the AUCs of the conventional methods (nomogram vs CP, MELD, and ALBI at P < 0.001, P < 0.005, and P < 0.005, respectively). In the validation cohort, the nomogram discrimination was also superior to those of the other three methods (AUC: 0.896; 95% CI, 0.774-1.000). The calibration curves showed good agreement in both cohorts, and the decision curve analysis of the entire cohort revealed that the nomogram was clinically useful. A pilot prospective analysis showed that the radiomics nomogram could predict PHLF with an AUC of 0.833 (95% CI, 0.591-1.000). CONCLUSIONS: A nomogram based on the Rad-score, MELD, and PS can predict PHLF.

A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy.

OBJECTIVE: The objective of the study was to develop and validate a radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula (POPF) in patients undergoing pancreaticoduodenectomy (PD). MATERIALS AND METHODS: A total of 117 consecutive patients who underwent PD were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography of the above patients. The least absolute shrinkage and selection operator logistic regression was used to construct a formula of Rad-score calculation. Then the performance of the formula was assessed with standard pancreatic Fistula Risk Score. RESULTS: The Rad-score could predict POPF with an area under the curve (AUC) of 0.8248 in the training cohort and of 0.7609 in the validation cohort. Patients who had experienced POPF generally had a statistically higher Rad-score than those who had not experienced POPF in both cohorts. The AUC of the Rad-score was statistically higher than the Fistula Risk Score for predicting POPF in both the training and validation cohort. CONCLUSION: A novel radiomics-based formula was developed and validated for predicting POPF in patients who underwent PD, which provides a new method for identifying POPF risks and may help to improve informed decision-making in the prevention of POPF at low cost.

Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p < 0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use.

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

PURPOSE: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. METHODS: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. RESULTS: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. CONCLUSIONS: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

A region growing vessel segmentation algorithm based on spectrum information.

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.