A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

Purpose: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

Liver segmentation based on Snakes Model and improved GrowCut algorithm in abdominal CT image.

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model.