Serous Membrane Detachment with Ultrasonic Homogenizer Improves Engraftment of Fetal Liver to Liver Surface in a Rat Model of Cirrhosis

Liver transplantation is the most effective treatment for end-stage cirrhosis. However, due to serious donor shortages, new treatments to replace liver transplantation are sorely needed. Recent studies have focused on novel therapeutic methods using hepatocytes and induced pluripotent stem cells, we try hard to develop methods for transplanting these cells to the liver surface. In the present study, we evaluated several methods for their efficiency in the detachment of serous membrane covering the liver surface for transplantation to the liver surface. The liver surface of dipeptidyl peptidase IV (DPPIV)-deficient rats in a cirrhosis model was detached by various methods, and then fetal livers from DPPIV-positive rats were transplanted. We found that the engraftment rate and area as well as the liver function were improved in rats undergoing transplantation following serous membrane detachment with an ultrasonic homogenizer, which mimics the Cavitron Ultrasonic Surgical Aspirator® (CUSA), compared with no detachment. Furthermore, the bleeding amount was lower with the ultrasonic homogenizer method than with the needle and electric scalpel methods. These findings provide evidence that transplantation to the liver surface with serous membrane detachment using CUSA might contribute to the development of new treatments for cirrhosis using cells or tissues.

Automatic, global registration in laparoscopic liver surgery

Purpose The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D–2D global registration in laparoscopic liver interventions. Methods Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. Results We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. Conclusions Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration.

Successful surgical rescue of delayed onset diaphragmatic hernia following radiofrequency ablation using a thoracoscopic approach for hepatocellular carcinoma: a case report

Background Radiofrequency ablation (RFA) is widely used as a minimally invasive treatment for hepatocellular carcinoma (HCC). RFA has a low risk of complications, especially compared with liver resection. Nevertheless, various complications have been reported after RFA for HCC; however, diaphragmatic hernia (DH) is extremely rare. Case presentation A 78-year-old man underwent thoracoscopic RFA for HCC located at the medial segment adjacent to the diaphragm approximately 7 years before being transported to the emergency department due complaints of nausea and abdominal pain. Computed tomography revealed a prolapsed small intestine through a defect in the right diaphragm, and emergency surgery was performed. The cause of diaphragmatic hernia was the scar of RFA. We confirmed that the small intestine had prolapsed into the right diaphragm, and we resected the necrotic small intestine and repaired the right diaphragm. Herein, we report a case of ileal strangulation due to diaphragmatic hernia after thoracoscopic RFA. Conclusions Care should be taken when performing thoracoscopic RFA, especially for tumors located on the liver surface adjacent to the diaphragm. Patients should be carefully followed up for possible DH, even after a long postoperative interval.

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

Improvement of a Tunable Stiffness Organ-Grasping Device by Design of a Wavy-Shaped Beam Structure

Tunable stiffness mechanisms can increase the noninvasiveness and stability of organ manipulation in laparoscopic liver resection. We have developed an organ-grasping device using beam-shaped tunable stiffness mechanism. Increasing the change ratio of stiffness will improve the performance of the device by offering high flexibility when adhering to the liver surface and high rigidity during the manipulation of the liver; however, optimal design of the beam has not been investigated. In this study, we investigate the wavy structure shape of the device that enhances the change in the ratio of stiffness. To increase the stiffness in a high-stiffness state, we used principal stress lines in the device to design the edge curve of the wavy shape material in the beams. We also investigated the arrangement of the wavy shape to decrease the stiffness in a low-stiffness state. Simulation using finite element method showed that the change ratio of stiffness was improved up to 13.0 by the new wavy shape arranged with the uniformly thick bottom of the waves.

Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy

Accurate localization and analyses of functional liver segments are crucial in devising various surgical procedures, including hepatectomy. To this end, they require the extraction of a liver from computed tomography, and then the identification of resection correspondence between individuals. The first part is usually impeded by inherent deficiencies, as present in medical images, and vast anatomical variations across subjects. While the model-based approach is found viable to tackle both issues, it is often undermined by an inadequate number of labeled samples, to capture all plausible variations. To address segmentation problems by balancing between accuracy, resource consumption, and data availability, this paper presents an efficient method for liver segmentation based on a graph-cut algorithm. One of its main novelties is the incorporation of a feature preserving a metric for boundary separation. Intuitive anatomical constraints are imposed to ensure valid extraction. The second part involves the symmetric conformal parameterization of the extracted liver surface onto a genus-0 domain. Provided with a few landmarks specified on two livers, we demonstrated that, by using a modified Beltrami differential, not only could they be non-rigidly registered, but also the hepatectomy on one liver could be envisioned on another. The merits of the proposed scheme were elucidated by both visual and numerical assessments on a standard MICCAI SLIVER07 dataset.

Importance of Laparoscopy to Solve the Diagnostic Dilemma of Abdominal Tuberculosis

Background: Diagnosis of abdominal tuberculosis as well as histopathological confirmation is difficult because of suboptimal access to the intraperitoneal pathology. Laparoscopy provides minimally invasive access to the peritoneal cavity and materials can be collected for confirmation of diagnosis. Objectives: To study the importance of laparoscopy as a tool for the diagnosis of abdominal tuberculosis and initiation of appropriate treatment without delay. Materials & Methods: In this study 25 patients with suspected abdominal tuberculosis were selected within the period of May, 2014 to October, 2014. Diagnostic laparoscopy performed on all patients with biopsy of tissue from accessible sites. Results: Diagnostic laparoscopy with biopsy confirmed the diagnosis in 24 (96%) patients, 23 of these patients (96%) had nodules at different site of abdominal cavity and 19 of these patients (76%) had ascites. In two cases there were nodules over liver surface; biopsy was taken also from both liver nodules. One nodule revealed fibrosis and another nodule revealed tuberculosis. Conclusion: Imaging and culture of ascitic fluid may fail to confirm or exclude abdominal tuberculosis in clinically suspected cases. Laparoscopy with peritoneal tissue biopsy provided rapid and correct diagnosis of abdominal tuberculosis and should be performed early in suspected cases. KYAMC Journal.2021;12(01): 14-17