Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
Science Foundation Ireland Centre for Research Training in Machine Learning（爱尔兰科学基金会机器学习研究培训中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
Fangyijie Wang, Guénolé Silvestre, Kathleen M. Curran
Accurate measurement of fetal head circumference is crucial for estimating fetal growth during routine prenatal screening. Prior to measurement, it is necessary to accurately identify and segment the region of interest, specifically the fetal head, in ultrasound images. Recent advancements in deep learning techniques have shown significant progress in segmenting the fetal head using encoder-decoder models. Among these models, U-Net has become a standard approach for accurate segmentation. However, training an encoder-decoder model can be a time-consuming process that demands substantial computational resources. Moreover, fine-tuning these models is particularly challenging when there is a limited amount of data available. There are still no "best-practice" guidelines for optimal fine-tuning of U-net for fetal ultrasound image segmentation. This work summarizes existing fine-tuning strategies with various backbone architectures, model components, and fine-tuning strategies across ultrasound data from Netherlands, Spain, Malawi, Egypt and Algeria. Our study shows that (1) fine-tuning U-Net leads to better performance than training from scratch, (2) fine-tuning strategies in decoder are superior to other strategies, (3) network architecture with less number of parameters can achieve similar or better performance. We also demonstrate the effectiveness of fine-tuning strategies in low-resource settings and further expand our experiments into few-shot learning. Lastly, we publicly released our code and specific fine-tuned weights.
Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；College of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
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 …
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of …
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）；School of Computer Science and Digital Technologies, Aston University（阿斯顿大学计算机科学与数字技术学院）
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University（暨南大学广东省中医信息技术重点实验室）；Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究学院（ICREA））
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network archit…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Affiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University（南方医科大学附属深圳妇幼保健院）
Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this…
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging. This procedure provides various information about fetus health and development, the progress of the pregnancy and, the baby's due date. Some of the biometric parameters of the fetus, like fetal head circumference (HC), must be measured to check the fetus's health and growth. In this paper, we investigated the effects of using multi-scale inputs in the network. We also propose a light convolutional neural network for automatic HC measurement. Experimental results on an ultrasound dataset of …
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
Taighde \' E ireann – Research Ireland Centre for Research Training in Machine Learning, Ireland School of Computer Science, University College Dublin, Ireland School of Medicine, University College Dublin, Ireland
Sequential Spatial-Temporal Network for Interpretable Automatic Ultrasonic Assessment of Fetal Head during labor
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Sydney School of Health Sciences, The University of Sydney（悉尼大学悉尼健康科学学院）；School of Science, Edith Cowan University（伊迪丝·考恩大学理学院）；Orange Hospital（奥兰治医院）
Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（牛津大学纳菲尔德妇女与生殖健康系）
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
Faculty of Computer Science Universitas Indonesia（印度尼西亚大学计算机科学学院）
The number of fetal-neonatal death in Indonesia is still high compared to developed countries. This is caused by the absence of maternal monitoring during pregnancy. This paper presents an automated measurement for fetal head circumference (HC) and abdominal circumference (AC) from the ultrasonography (USG) image. This automated measurement is beneficial to detect early fetal abnormalities during the pregnancy period. We used the convolutional neural network (CNN) method, to preprocess the USG data. After that, we approximate the head and abdominal circumference using the Hough transform algor…
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fréchet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality score…
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation
Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）；Indian Institute of Space Science & Technology（印度空间科学技术学院）；Indian Institute of Public Health（印度公共卫生学院）
Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. …
This work explores a hybrid approach to segmentation as an alternative to a purely data-driven approach. We introduce an end-to-end U-Net based network called DU-Net, which uses additional frequency preserving features, namely the Scattering Coefficients (SC), for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts on four datasets and two segmentation tasks: Optic Disc and Optic Cup in color fundus images and fetal Head in ultrasound images. The proposed method shows remarkable impro…
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jinan University（暨南大学）；The University of Auckland（奥克兰大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；The Hong Kong University of Science and Technology（香港科技大学）；University of Girona（赫罗纳大学）；Canon Medical Systems (China) Co., LTD（佳能医疗系统（中国）有限公司）；Nankai University（南开大学）；Hunan University（湖南大学）；University College Dublin（都柏林大学学院）；Zhejiang Gongshang University（浙江工商大学）；Chongqing University of Technology（重庆理工大学）；Shenzhen University（深圳大学）；NanFang Hospital of Southern Medical University（南方医科大学南方医院）；Zhujiang Hospital of Southern Medical University（南方医科大学珠江医院）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究机构）
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Sidra Medical and Research Center（西德拉医疗与研究中心）；University Sains Malaysia（马来西亚理科大学）
Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEE…
In this study, we combine a standardized acquisition protocol with image analysis algorithms to investigate if it is possible to automatically detect maternal risk factors without a trained sonographer. The standardized acquisition protocol can be taught to any health care worker within two hours. This protocol was acquired from 280 pregnant women at St.\ Luke's Catholic Hospital, Wolisso, Ethiopia. A VGG-like network was used to perform a frame classification for each frame within the acquired ultrasound data. This frame classification was used to automatically determine the number of fetuses…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；NAAMII（NAAMII研究所）
Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound exami…
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a uni…
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are requ…
University of Oxford（牛津大学）；Amazon（亚马逊）；Kings College London（伦敦国王学院）
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturb…
Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made …
Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images
Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Visual Geometry Group, Department of Engineering Science, University of Oxford（牛津大学工程科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算科学与数据科学学院）
CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies
London South Bank University（伦敦南岸大学）；University of Padova（帕多瓦大学）
ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI
Department of Biomedical Physics and Technology, University of Dhaka（达卡大学生物医学物理与技术系）；Department of Electrical Engineering, College of Engineering, Qatar University（卡塔尔大学工程学院电气工程系）；Department of Biomedical Engineering, Jashore University of Science and Technology（贾沙尔大学科学与技术学院生物医学工程系）
Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（医学超声技术国家-地区关键工程技术实验室）；School of Biomedical Engineering（生物医学工程学院）；College of Computer Science and Software Engineering（计算机科学与软件工程学院）；Medical Ultrasound lmage Computing (MUSIC) Lab（医学超声图像计算（MUSIC）实验室）；School of Medicine（医学院）；The First Affiliated Hospital of Xiamen University（厦门大学第一附属医院）；Shenzhen Baoan Women's and Children's Hospital（深圳宝安妇幼医院）；University of California, Berkeley（加州大学伯克利分校）；Yongkang Maternal and Child Health Hospital（永康 maternal and child health hospital）；Shenzhen RayShape Medical Technology Co., Ltd（深圳RayShape医疗科技有限公司）
University of California Berkeley（加州大学伯克利分校）；University of California San Francisco School of Medicine（加州大学旧金山分校医学院）
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans. We propose to solve both tasks as a semantic segmentation problem using a patch-based fully convolutional network (PatchFCN). This formulation allows us to accurately localize hemorrhages while bypassing the complexity of object detection. Our system demonstrates competitive performance with a human expert and the state-of-the-art on classification tasks (0.976, 0.966 AUC of ROC on retrospective and prospective test sets) and on segmentation tasks (0.785 pixel AP, 0.…
In this study, segmentation of quadriceps muscle heads of ultra-endurance athletes was done using a multi-atlas segmentation and corrective leaning framework where the registration based multi-atlas segmentation step was replaced with weakly supervised U-Net. For the case with remarkably different morphology, our method produced improved accuracy, while reduced significantly the computation time.
Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal care throughout pregnancy and identifying complications such as fetal growth disorders. Since derivation of GA from manual fetal biometry measurements (head, abdomen, femur) are operator-dependent and time-consuming, there have been a number of research efforts focused on using artificial intelligence (AI) models to estimate GA using standard biometry images, but there is still room to improve the accuracy and reliabil…
Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.
Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；State University of New York at Stony Brook（纽约州立大学石溪分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital Zhejiang University（浙江大学医学院附属第一医院）
Efficient Ultrasound Image Segmentation with Token-Conditioned Neural Cellular Automata
School of Medicine, University College Dublin（都柏林大学医学院）；Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究培训中心）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）；Department of Computer Science, Maynooth University（梅努斯大学计算机科学系）；Departament de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机科学系）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究机构）
