An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( <1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

Systematic pelvic and aortic lymphadenectomy in advanced ovarian cancer patients at the time of interval debulking surgery: a double-institution case-control study

The prognostic role of systematic lymphadenectomy remains unclear in advanced ovarian cancer (AOC). Only few retrospective case series have investigated the percentage of lymph node metastases after neoadjuvant chemotherapy. This multi-institutional case-control study analyzed the prognostic role of systematic lymphadenectomy in AOC patients at the time of interval debulking surgery (IDS)

Comparison of dust transport modelling codes in a tokamak plasma

Since the installation on the Joint European Torus of the ITER-like Wall (ILW), intense radiation spikes have been observed, especially in the discharges following a disruption, and have been associated with possible sudden injection of tungsten (W) impurities consequent to full ablation of W dust particles. The problem of dust production, mobilization, and interaction both with the plasma and the vessel tiles is therefore of great concern and requires the setting up of dedicated and validated numerical modeling tools. Among these, a useful role is played by the dust trajectory calculators, which can present in a relatively clear way the qualitative and quantitative description of the mobilization and fate of selected bunches of dust grains