8 research outputs found
Automatic Segmentation of Human Placenta Images with U-Net
© 2013 IEEE. Placenta is closely related to the health of the fetus. Abnormal placental function will affect the normal development of the fetus, and in severe cases, even endanger the life of the fetus. Therefore, accurate and quantitative evaluation of placenta has important clinical significance. It is a common method to segment human placenta with semantic segmentation. However, manual segmentation relies too much on the professional knowledge and clinical experience of the staff, and it will also consume a lot of time. Therefore, based on u-net, we propose an automatic segmentation method of human placenta, which reduces manual intervention and greatly speeds up the segmentation, making large-scale segmentation possible. The human placenta data set we used was labeled by experts, which was obtained from prenatal examinations of 11 pregnant women, about 1,110 images. It was a comprehensive and clinically significant data set. By training the network with such data set, the robustness of the model will be better. After testing on the data set, the segmentation effect is basically consistent with the manual segmentation effect
AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation
Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound
Fetal pose estimation in 3D ultrasound (US) involves identifying a set of
associated fetal anatomical landmarks. Its primary objective is to provide
comprehensive information about the fetus through landmark connections, thus
benefiting various critical applications, such as biometric measurements, plane
localization, and fetal movement monitoring. However, accurately estimating the
3D fetal pose in US volume has several challenges, including poor image
quality, limited GPU memory for tackling high dimensional data, symmetrical or
ambiguous anatomical structures, and considerable variations in fetal poses. In
this study, we propose a novel 3D fetal pose estimation framework (called
FetusMapV2) to overcome the above challenges. Our contribution is three-fold.
First, we propose a heuristic scheme that explores the complementary network
structure-unconstrained and activation-unreserved GPU memory management
approaches, which can enlarge the input image resolution for better results
under limited GPU memory. Second, we design a novel Pair Loss to mitigate
confusion caused by symmetrical and similar anatomical structures. It separates
the hidden classification task from the landmark localization task and thus
progressively eases model learning. Last, we propose a shape priors-based
self-supervised learning by selecting the relatively stable landmarks to refine
the pose online. Extensive experiments and diverse applications on a
large-scale fetal US dataset including 1000 volumes with 22 landmarks per
volume demonstrate that our method outperforms other strong competitors.Comment: 16 pages, 11 figures, accepted by Medical Image Analysis(2023
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer
vision with applications in autonomous driving, robotics, augmented reality and
medical image analysis. It has received significant attention from the computer
vision, graphics and machine learning communities. Traditionally, 3D
segmentation was performed with hand-crafted features and engineered methods
which failed to achieve acceptable accuracy and could not generalize to
large-scale data. Driven by their great success in 2D computer vision, deep
learning techniques have recently become the tool of choice for 3D segmentation
tasks as well. This has led to an influx of a large number of methods in the
literature that have been evaluated on different benchmark datasets. This paper
provides a comprehensive survey of recent progress in deep learning based 3D
segmentation covering over 150 papers. It summarizes the most commonly used
pipelines, discusses their highlights and shortcomings, and analyzes the
competitive results of these segmentation methods. Based on the analysis, it
also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure