Caroline L. Knight

Bio: Caroline L. Knight is an academic researcher from King's College London. The author has contributed to research in topics: Pregnancy & Transformation (function). The author has an hindex of 10, co-authored 26 publications receiving 295 citations. Previous affiliations of Caroline L. Knight include Analysis Group & Guy's and St Thomas' NHS Foundation Trust.

Management of systemic lupus erythematosus during pregnancy: challenges and solutions.

Abstract: Systemic lupus erythematosus (SLE) is a chronic, multisystem autoimmune disease predominantly affecting women, particularly those of childbearing age. SLE provides challenges in the prepregnancy, antenatal, intrapartum, and postpartum periods for these women, and for the medical, obstetric, and midwifery teams who provide their care. As with many medical conditions in pregnancy, the best maternal and fetal-neonatal outcomes are obtained with a planned pregnancy and a cohesive multidisciplinary approach. Effective prepregnancy risk assessment and counseling includes exploration of factors for poor pregnancy outcome, discussion of risks, and appropriate planning for pregnancy, with consideration of discussion of relative contraindications to pregnancy. In pregnancy, early referral for hospital-coordinated care, involvement of obstetricians and rheumatologists (and other specialists as required), an individual management plan, regular reviews, and early recognition of flares and complications are all important. Women are at risk of lupus flares, worsening renal impairment, onset of or worsening hypertension, preeclampsia, and/or venous thromboembolism, and miscarriage, intrauterine growth restriction, preterm delivery, and/or neonatal lupus syndrome (congenital heart block or neonatal lupus erythematosus). A cesarean section may be required in certain obstetric contexts (such as urgent preterm delivery for maternal and/or fetal well-being), but vaginal birth should be the aim for the majority of women. Postnatally, an ongoing individual management plan remains important, with neonatal management where necessary and rheumatology followup. This article explores the challenges at each stage of pregnancy, discusses the effect of SLE on pregnancy and vice versa, and reviews antirheumatic medications with the latest guidance about their use and safety in pregnancy. Such information is required to effectively and safely manage each stage of pregnancy in women with SLE.

Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

Abstract: Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model’s performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

Abstract: In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model's reasoning process, which can also be used for weakly supervised object localisation.

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.

Abstract: We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our approach adopts a multi-task learning framework that combines regression and classification to improve localisation accuracy. We extend PIN to localise multiple landmarks by using principal component analysis, which models the global anatomical relationships between landmarks. We have evaluated PIN using 72 3D ultrasound images from fetal screening examinations. PIN achieves quantitatively an average landmark localisation error of 5.59 mm and a runtime of 0.44 s to predict 10 landmarks per volume. Qualitatively, anatomical 2D standard scan planes derived from the predicted landmark locations are visually similar to the clinical ground truth.

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

Abstract: Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for the transventricular and transcerebellar planes respectively and takes 0.46s per plane. Source code is publicly available at this https URL.