Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor

Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation

Three-dimensional (3D) ultrasound imaging has contributed to our understanding of fetal developmental processes by providing rich contextual information of the inherently 3D anatomies. However, its use is limited in clinical settings, due to the high purchasing costs and limited diagnostic practicality. Freehand 2D ultrasound imaging, in contrast, is routinely used in standard obstetric exams, but inherently lacks a 3D representation of the anatomies, which limits its potential for more advanced assessment. Such full representations are challenging to recover even with external tracking devices due to internal fetal movement which is independent from the operator-led trajectory of the probe. Capitalizing on the flexibility offered by freehand 2D ultrasound acquisition, we propose ImplicitVol to reconstruct 3D volumes from non-sensor-tracked 2D ultrasound sweeps. Conventionally, reconstructions are performed on a discrete voxel grid. We, however, employ a deep neural network to represent, for the first time, the reconstructed volume as an implicit function. Specifically, ImplicitVol takes a set of 2D images as input, predicts their locations in 3D space, jointly refines the inferred locations, and learns a full volumetric reconstruction. When testing natively-acquired and volume-sampled 2D ultrasound video sequences collected from different manufacturers, the 3D volumes reconstructed by ImplicitVol show significantly better visual and semantic quality than the existing interpolation-based reconstruction approaches. The inherent continuity of implicit representation also enables ImplicitVol to reconstruct the volume to arbitrarily high resolutions. As formulated, ImplicitVol has the potential to integrate seamlessly into the clinical workflow, while providing richer information for diagnosis and evaluation of the developing brain.

Artificial intelligence in obstetric ultrasound: A scoping review

The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords "neural networks", OR "artificial intelligence", OR "machine learning", OR "deep learning", AND "obstetrics", OR "obstetrical", OR "fetus", OR "foetus", OR "fetal", OR "foetal", OR "pregnancy", or "pregnant", AND "ultrasound" from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors' search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

A review of image processing methods for fetal head and brain analysis in ultrasound images

BACKGROUND AND OBJECTIVE

Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods.

METHODS

In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement.

RESULTS

For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications.

CONCLUSIONS

A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.

Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study

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PET images features are more stable across different delineation of the same target.

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Shape family features are more stable.

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The survival model based on Dedicated contours achieved better performance for predicting PFS.

A vast majority of studies in the radiomics field are based on contours originating from radiotherapy planning. This kind of delineation (e.g. Gross Tumor Volume, GTV) is often larger than the true tumoral volume, sometimes including parts of other organs (e.g. trachea in Head and Neck, H&N studies) and the impact of such over-segmentation was little investigated so far. In this paper, we propose to evaluate and compare the performance between models using two contour types: those from radiotherapy planning, and those specifically delineated for radiomics studies. For the latter, we modified the radiotherapy contours to fit the true tumoral volume. The two contour types were compared when predicting Progression-Free Survival (PFS) using Cox models based on radiomics features extracted from FluoroDeoxyGlucose-Positron Emission Tomography (FDG-PET) and CT images of 239 patients with oropharyngeal H&N cancer collected from five centers, the data from the 2020 HECKTOR challenge. Using Dedicated contours demonstrated better performance for predicting PFS, where Harell’s concordance indices of 0.61 and 0.69 were achieved for Radiotherapy and Dedicated contours, respectively. Using automatically Resegmented contours based on a fixed intensity range was associated with a C-index of 0.63. These results illustrate the importance of using clean dedicated contours that are close to the true tumoral volume in radiomics studies, even when tumor contours are already available from radiotherapy treatment planning

A FDG-PET radiomics signature detects esophageal squamous cell carcinoma patients who do not benefit from chemoradiation

Detection of patients with esophageal squamous cell carcinoma (ESCC) who do not benefit from standard chemoradiation (CRT) is an important medical need. Radiomics using 18-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a promising approach. In this retrospective study of 184 patients with locally advanced ESCC. 152 patients from one center were grouped into a training cohort (n = 100) and an internal validation cohort (n = 52). External validation was performed with 32 patients treated at a second center. Primary endpoint was disease-free survival (DFS), secondary endpoints were overall survival (OS) and local control (LC). FDG-PET radiomics features were selected by Lasso-Cox regression analyses and a separate radiomics signature was calculated for each endpoint. In the training cohort radiomics signatures containing up to four PET derived features were able to identify non-responders in regard of all endpoints (DFS p < 0.001, LC p = 0.003, OS p = 0.001). After successful internal validation of the cutoff values generated by the training cohort for DFS (p = 0.025) and OS (p = 0.002), external validation using these cutoffs was successful for DFS (p = 0.002) but not for the other investigated endpoints. These results suggest that pre-treatment FDG-PET features may be useful to detect patients who do not respond to CRT and could benefit from alternative treatment.

Comparison of MRI and neurosonogram 1- and 2-dimensional morphological measurements of the newborn corpus callosum

BACKGROUND

Developmental abnormalities of the corpus callosum (CC) are linked to multiple neuro-developmental disorders, for which neonatal neuroimaging may allow earlier diagnosis and intervention. MRI is often considered the most sensitive imaging modality to white matter changes, while neurosonogram (NS) remains the clinical staple. This study assesses the correlation between MRI and US measurements of the neonatal CC using a protocol derived from established methodologies.

METHODS

MR and NS images from an existing cohort of term infants (≥37 weeks gestational age) were studied. Length and area measurements of the CC made with linear (LUS) and phased array US (PUS) data were compared to those from MRI. Intra-observer reliabilities were estimated.

RESULTS

Moderate-to-strong correlation strengths were observed for length measurements and the total area of the CC. Sectional area measurements showed poorer correlations. Bland-Altman plots support improved correspondence of length and total area measurements. LUS data appeared to correspond closer to MRI. All three modalities showed comparable repeatability.

CONCLUSION

NS correlates well with some MRI measurements of the CC and shows similar levels of repeatability, making them possibly interchangeable. Use of LUS, a technique rarely used for NS, may be preferable to the standard approach for morphological studies.