Multi-region saliency-aware learning for cross-domain placenta image segmentation

A Multi-Stage Feature Aggregation and Structure Awareness Network for Concrete Bridge Crack Detection

One of the most significant problems affecting a concrete bridge's safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features effectively, fail to perceive the long-range dependencies between crack regions, and have weak suppression ability for background noises, leading to low detection precision of bridge cracks. To address this problem, a multi-stage feature aggregation and structure awareness network (MFSA-Net) for pixel-level concrete bridge crack detection is proposed in this paper. Specifically, in the coding stage, a structure-aware convolution block is proposed by combining square convolution with strip convolution to perceive the linear structure of concrete bridge cracks. Square convolution is used to capture detailed local information. In contrast, strip convolution is employed to interact with the local features to establish the long-range dependence relationship between discrete crack regions. Unlike the self-attention mechanism, strip convolution also suppresses background interference near crack regions. Meanwhile, the feature attention fusion block is presented for fusing features from the encoder and decoder at the same stage, which can sharpen the edges of concrete bridge cracks. In order to fully utilize the shallow detail features and deep semantic features, the features from different stages are aggregated to obtain fine-grained segmentation results. The proposed MFSA-Net was trained and evaluated on the publicly available concrete bridge crack dataset and achieved average results of 73.74%, 77.04%, 75.30%, and 60.48% for precision, recall, F1 score, and IoU, respectively, on three typical sub-datasets, thus showing optimal performance in comparison with other existing methods. MFSA-Net also gained optimal performance on two publicly available concrete pavement crack datasets, thereby indicating its adaptability to crack detection across diverse scenarios.

Placental MRI segmentation based on multi-receptive field and mixed attention separation mechanism

OBJECTIVE

To reduce the occurrence of massive bleeding during placental abruption in patients with placenta accrete, we established a medical imaging based on multi-receptive field and mixed attention separation mechanism (MRF-MAS) model to improve the accuracy of MRI placenta segmentation and provide a basis for subsequent placenta accreta.

METHODS

We propose a placenta MRI segmentation technology using the MRF-MAS framework to develop a medical image diagnostic technique. The model first uses the multi-receptive field feature structure to obtain multi-level information, and improves the expression of features at differing scales. Note that the hybrid attention mechanism combines channel attention and spatial attention, separates the input feature sets and computes the attention separately, and finally reorganizes the feature maps. To show that the model can improve the accuracy of segmenting the placenta, we adopt mean Intersection over Union (IoU), Dice similarity coefficient (Dice) and area under the receiver operating characteristic curve (AUC) with U-Net, Mask RCNN, Deeplab v3 for comparison.

RESULTS

The four models achieved different outcomes based on our placenta dataset, with our model IoU and Dice up to 0.8169 and 0.8992, which are 5.51% and 3.03% higher than the average of the three comparison models.

CONCLUSION

The model proposed by us is helpful to assist the imaging diagnosis and at the same time provides a quantitative reference for the precise treatment of placenta accreta, assists the Equationtion of the clinical operation plan of the physician, and promotes the precision medicine of placenta accreta.

Placental pathology reports: A qualitative study in a US university hospital setting on perceived clinical utility and areas for improvement

OBJECTIVE

To explore how placental pathology is currently used by clinicians and what placental information would be most useful in the immediate hours after delivery.

STUDY DESIGN

We used a qualitative study design to conduct in-depth, semi-structured interviews with obstetric and neonatal clinicians who provide delivery or postpartum care at an academic medical center in the US (n = 19). Interviews were transcribed and analyzed using descriptive content analysis.

RESULTS

Clinicians valued placental pathology information yet cited multiple barriers that prevent the consistent use of pathology. Four main themes were identified. First, the placenta is sent to pathology for consistent reasons, however, the pathology report is accessed by clinicians inconsistently due to key barriers: difficult to find in the electronic medical record, understand, and get quickly. Second, clinicians value placental pathology for explanatory capability as well as for contributions to current and future care, particularly when there is fetal growth restriction, stillbirth, or antibiotic use. Third, a rapid placental exam (specifically including placental weight, infection, infarction, and overall assessment) would be helpful in providing clinical care. Fourth, placental pathology reports that connect clinically relevant findings (similar to radiology) and that are written with plain, standardized language and that non-pathologists can more readily understand are preferred.

CONCLUSION

Placental pathology is important to clinicians that care for mothers and newborns (particularly those that are critically ill) after birth, yet many problems stand in the way of its usefulness. Hospital administrators, perinatal pathologists, and clinicians should work together to improve access to and contents of reports. Support for new methods to provide quick placenta information is warranted.

Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images

Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.

Chorioamnionitis and Risk for Maternal and Neonatal Sepsis: A Systematic Review and Meta-analysis

OBJECTIVE

To estimate the risk of maternal and neonatal sepsis associated with chorioamnionitis.

DATA SOURCES

PubMed, BIOSIS, and ClinicalTrials.gov databases were systematically searched for full-text articles in English from inception until May 11, 2020.

METHODS OF STUDY SELECTION

We screened 1,251 studies. Randomized controlled trials, case-control, or cohort studies quantifying a relationship between chorioamnionitis and sepsis in mothers (postpartum) or neonates born at greater than 22 weeks of gestation were eligible. Studies were grouped for meta-analyses according to exposures of histologic or clinical chorioamnionitis and outcomes of maternal or neonatal sepsis.

TABULATION, INTEGRATION, AND RESULTS

One hundred three studies were included, and 55 met criteria for meta-analysis (39 studies of preterm neonates, 10 studies of general populations of preterm and term neonates, and six studies of late preterm and term neonates). Study details and quantitative data were abstracted. Random-effects models were used to generate pooled odds ratios (ORs); most studies only reported unadjusted results. Histologic chorioamnionitis was associated with confirmed and any early-onset neonatal sepsis (unadjusted pooled ORs 4.42 [95% CI 2.68-7.29] and 5.88 [95% CI 3.68-9.41], respectively). Clinical chorioamnionitis was also associated with confirmed and any early-onset neonatal sepsis (unadjusted pooled ORs 6.82 [95% CI 4.93-9.45] and 3.90 [95% CI 2.74-5.55], respectively). Additionally, histologic and clinical chorioamnionitis were each associated with higher odds of late-onset sepsis in preterm neonates. Confirmed sepsis incidence was 7% (early-onset) and 22% (late-onset) for histologic and 6% (early-onset) and 26% (late-onset) for clinical chorioamnionitis-exposed neonates. Three studies evaluated chorioamnionitis and maternal sepsis and were inconclusive.

CONCLUSION

Both histologic and clinical chorioamnionitis were associated with early- and late-onset sepsis in neonates. Overall, our findings support current guidelines for preventative neonatal care. There was insufficient evidence to determine the association between chorioamnionitis and maternal sepsis.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42020156812.