Accuracy and Reliability of Pelvimetry Measures Obtained by Manual or Automatic Labeling of Three-Dimensional Pelvic Models.

(1) Background: The morphology of the pelvic cavity is important for decision-making in obstetrics. This study aimed to estimate the accuracy and reliability of pelvimetry measures obtained when radiologists manually label anatomical landmarks on three-dimensional (3D) pelvic models. A second objective was to design an automatic labeling method. (2) Methods: Three operators segmented 10 computed tomography scans each. Three radiologists then labeled 12 anatomical landmarks on the pelvic models, which allowed for the calculation of 15 pelvimetry measures. Additionally, an automatic labeling method was developed based on a reference pelvic model, including reference anatomical landmarks, matching the individual pelvic models. (3) Results: Heterogeneity among landmarks in radiologists' labeling accuracy was observed, with some landmarks being rarely mislabeled by more than 4 mm and others being frequently mislabeled by 10 mm or more. The propagation to the pelvimetry measures was limited; only one out of the 15 measures reported a median error above 5 mm or 5°, and all measures showed moderate to excellent inter-radiologist reliability. The automatic method outperformed manual labeling. (4) Conclusions: This study confirmed the suitability of pelvimetry measures based on manual labeling of 3D pelvic models. Automatic labeling offers promising perspectives to decrease the demand on radiologists, standardize the labeling, and describe the pelvic cavity in more detail.

3-D Freehand Ultrasound Calibration Using a Tissue-Mimicking Phantom With Parallel Wires.

This article describes a method used to calibrate 3-D freehand ultrasound systems based on phantoms with parallel wires forming two perpendicular planes, such as the usual general-purpose commercial phantoms. In our algorithm, the phantom pose is co-optimized with the calibration to avoid the need to precisely track the phantom. We provide a geometrical analysis to explain the proposed acquisition protocol. Finally, we give an estimate of the system accuracy and precision based on measurements acquired on an independent test phantom. We obtained error norms of 1.6 mm up to 6 cm of depth and 3.5 mm between 6 and 14 cm of depth, in total average. In conclusion, it is possible to calibrate ultrasound tracked-probe systems with a reasonable accuracy based on a general-purpose phantom. Contrarily to most calibration methods that imply the construction of the phantom, the present algorithm is based on a standard phantom geometry that is commercially available.

'connEcted caesarean section': creating a virtual link between MOthers and their infanTs to ImprOve maternal childbirth experieNce - study protocol for a PILOT trial (e-motion-pilot).

INTRODUCTION: One-third of mothers rate their childbirth as traumatic. The prevalence of childbirth-related post-traumatic stress disorder (CB-PTSD) is 4.7%. Skin-to-skin contact is a protective factor against CB-PTSD. However, during a caesarean section (CS), skin-to-skin contact is not always feasible and mothers and infants are often separated. In those cases, there is no validated and available solution to substitute this unique protective factor. Based on the results of studies using virtual reality and head-mounted displays (HMDs) and studies on childbirth experience, we hypothesise that enabling the mother to have a visual and auditory contact with her baby could improve her childbirth experience while she and her baby are separated. To facilitate this connection, we will use a two-dimensional 360° camera filming the baby linked securely to an HMD that the mother can wear during the end of the surgery. METHODS AND ANALYSIS: This study protocol describes a monocentric open-label controlled pilot trial with minimal risk testing the effects of a visual and auditory contact via an HMD worn by the mother airing a live video of her newborn compared with treatment-as-usual in 70 women after CS. The first 35 consecutive participants will be the control group and will receive the standard care. The next 35 consecutive participants will have the intervention. The primary outcome will be differences in maternal childbirth experience (Childbirth Experience Questionnaire 2) at 1-week postpartum between the intervention and control groups. Secondary outcomes will be CB-PTSD symptoms, birth satisfaction, mother-infant bonding, perceived pain and stress during childbirth, maternal anxiety and depression symptoms, anaesthesiological data and acceptability of the procedure. ETHICS AND DISSEMINATION: Ethics approval was granted by the Human Research Ethics Committee of the Canton de Vaud (study number 2022-00215). Dissemination of results will occur via national and international conferences, peer-reviewed journals, public conferences and social media. TRIAL REGISTRATION NUMBER: NCT05319665.

Generative Adversarial Networks Improve the Reproducibility and Discriminative Power of Radiomic Features.

PURPOSE: To assess the contribution of a generative adversarial network (GAN) to improve intermanufacturer reproducibility of radiomic features (RFs). MATERIALS AND METHODS: The authors retrospectively developed a cycle-GAN to translate texture information from chest radiographs acquired using one manufacturer (Siemens) to chest radiographs acquired using another (Philips), producing fake chest radiographs with different textures. The authors prospectively evaluated the ability of this texture-translation cycle-GAN to reduce the intermanufacturer variability of RFs extracted from the lung parenchyma. This study assessed the cycle-GAN's ability to fool several machine learning (ML) classifiers tasked with recognizing the manufacturer on the basis of chest radiography inputs. The authors also evaluated the cycle-GAN's ability to mislead radiologists who were asked to perform the same recognition task. Finally, the authors tested whether the cycle-GAN had an impact on radiomic diagnostic accuracy for chest radiography in patients with congestive heart failure (CHF). RESULTS: RFs, extracted from chest radiographs after the cycle-GAN's texture translation (fake chest radiographs), showed decreased intermanufacturer RF variability. Using cycle-GAN-generated chest radiographs as inputs, ML classifiers categorized the fake chest radiographs as belonging to the target manufacturer rather than to a native one. Moreover, cycle-GAN fooled two experienced radiologists who identified fake chest radiographs as belonging to a target manufacturer class. Finally, reducing intermanufacturer RF variability with cycle-GAN improved the discriminative power of RFs for patients without CHF versus patients with CHF (from 55% to 73.5%, P < .001). CONCLUSION: Both ML classifiers and radiologists had difficulty recognizing the chest radiographs' manufacturer. The cycle-GAN improved RF intermanufacturer reproducibility and discriminative power for identifying patients with CHF. This deep learning approach may help counteract the sensitivity of RFs to differences in acquisition.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Alderson in this issue.