P068 ROBOTIC REPAIR OF VENTRAL AND INCISIONAL HERNIAS: A STUDY ON 118 PATIENTS OPERATED BY RV-TAPP AND R-RIVES

Aim The purpose of this study is to compare the results of robotic ventral TAPP and robotic retrorectus repair for ventral and incisional hernias. Material and Methods The results of 118 consecutive rv-TAPP (88) and r-Rives (30) surgeries are presented. The study was approved by the ethics committee (Ref. No. 2019-02046). Primary ventral hernias were treated mainly by rv-TAPP approach, incisional hernias by r-Rives Technique. Patients were followed up six weeks postoperatively. Results In every third patient, an additional finding at the linea alba was found. Patients in the r-Rives group were significantly older (p = 0.001). Hernia gaps were significantly larger and meshes were significantly larger in the r-Rives group (p < 0.001). The ratio of mesh area to hernia gap area was comparable in both groups (p = 0.142). OR time was significantly shorter for rv-TAPP (82min) than r-Rives (109min). Hospital stay was shorter in the rv-TAPP group than in the r-Rives group (1.5 vs. 2.7 days, respectively) (p < 0.001). There was a significant clustering of type II seromas in the r-Rives group (p < 0.001), however, the total number of seromas was comparable. Conclusions rv-TAPP and r-Rives have the advantages of minimally invasive procedures (low complication rate) and most of the advantages of open procedures (morphological reconstruction). Both techniques allow consistent extraperitonealization of meshes. Umbilical and epigastric hernias (<4cm) are treated as rv-TAPP; incisional hernias, large hernia gaps (4-7cm), as well as in case of planned suturing of the linea alba, the r-Rives is indicated. Concomitant hernia gaps of the linea alba are also treated. Both procedures have few complications and are suitable for residents training.

Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ . By dynamically changing the impact factor ρ , the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ , we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.

Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons

Motivation To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast and the large dynamic range of signal and background both within and across images. Results We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high signal-background contrast and better within- and between image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. Additionally, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased 5 times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience providing better 3D morphological reconstruction and lower cost of data storage and transfer. Availability The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality, and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). Supplementary information Supplementary data are available at Bioinformatics online.

Evaluation of the Level of Fit of Radial Fracture Fixation Plates Using Virtual and 3D-printed Models

Radial fractures often require surgical stabilization with fracture fixation plates. Incomplete morphological reconstruction was linked to poor outcome such as limited forearm rotation. Pre-contoured plates are often used, but large inter-subject morphological variations may result in poor fit. Therefore, the goal of this study was to develop a reliable virtual measure of plate-to-bone fit. In addition, the study evaluated the accuracy with which 3D printed bones reproduce the morphology of the physical radius. Virtual models and 3D-printed models of six cadaver radii were produced from bone scans. Level of fit of pre-contoured plates were measured in three ways: directly on pre-contoured physical plates fitted to cadaver bone; pre-contoured physical plates fitted to 3D printed bone; and virtual plate models fitted to virtual bone models. In addition, the study evaluated the accuracy with which 3D printed bone reproduces the physical bone morphology. The results indicate excellent agreement between the physical and virtual level of fit measures as well as excellent geometrical accuracy of the 3D-printed bones. These provide the necessary foundation for guiding the development of better fitted pre-contoured fracture fixation plates as well as for developing pre-surgically patient specific pre-contoured plates.