9 research outputs found
Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. In this work, we describe the current methods, the architectures of deep convolutional neural networks used in CBCT. Literature from 2000-2020 from the PubMed database, Google Scholar, was analyzed. Account has been taken of publications in English that describe architectures of deep convolutional neural networks used in CBCT. The results of the reviewed studies indicate that deep learning methods employed in orthodontics can be far superior in comparison to other high-performing algorithms
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
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 required to
find the appropriate acquisition plane and perform accurate measurements of the
fetus. In addition, existing computer-aided methods for fetal US biometric
measurement address only one single image frame without considering temporal
features. To address these shortcomings, we propose an end-to-end multi-task
neural network for spatio-temporal ultrasound scan video analysis to
simultaneously localize, classify and measure the fetal body parts. We propose
a new encoder-decoder segmentation architecture that incorporates a
classification branch. Additionally, we employ an attention mechanism with a
stacked module to learn salient maps to suppress irrelevant US regions and
efficient scan plane localization. We trained on the fetal ultrasound video
comes from routine examinations of 700 different patients. Our method called
FetalNet outperforms existing state-of-the-art methods in both classification
and segmentation in fetal ultrasound video recordings.Comment: Accepted to 28th International Conference on Neural Information
Processing (ICONIP) 2021, Bali, Indonesia, 8-12 December, 202
TabAttention: Learning Attention Conditionally on Tabular Data
Medical data analysis often combines both imaging and tabular data processing
using machine learning algorithms. While previous studies have investigated the
impact of attention mechanisms on deep learning models, few have explored
integrating attention modules and tabular data. In this paper, we introduce
TabAttention, a novel module that enhances the performance of Convolutional
Neural Networks (CNNs) with an attention mechanism that is trained
conditionally on tabular data. Specifically, we extend the Convolutional Block
Attention Module to 3D by adding a Temporal Attention Module that uses
multi-head self-attention to learn attention maps. Furthermore, we enhance all
attention modules by integrating tabular data embeddings. Our approach is
demonstrated on the fetal birth weight (FBW) estimation task, using 92 fetal
abdominal ultrasound video scans and fetal biometry measurements. Our results
indicate that TabAttention outperforms clinicians and existing methods that
rely on tabular and/or imaging data for FBW prediction. This novel approach has
the potential to improve computer-aided diagnosis in various clinical workflows
where imaging and tabular data are combined. We provide a source code for
integrating TabAttention in CNNs at
https://github.com/SanoScience/Tab-Attention.Comment: Accepted for the 26th International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI) 202
Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements
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 by
five experienced sonographers at two time points separated by at least two
weeks. Intra- and inter-observer variabilities were estimated. Main results. We
found that automated fetal biometric measurements obtained by FUVAI were
comparable to the measurements performed by experienced sonographers The
observed differences in measurement values were within the range of inter- and
intra-observer variability. Moreover, analysis has shown that these differences
were not statistically significant when comparing any individual medical expert
to our model. Significance. We argue that FUVAI has the potential to assist
sonographers who perform fetal biometric measurements in clinical settings by
providing them with suggestions regarding the best measuring frames, along with
automated measurements. Moreover, FUVAI is able perform these tasks in just a
few seconds, which is a huge difference compared to the average of six minutes
taken by sonographers. This is significant, given the shortage of medical
experts capable of interpreting fetal ultrasound images in numerous countries.Comment: Published at Physics in Medicine & Biolog
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Predicting fetal weight at birth is an important aspect of perinatal care,
particularly in the context of antenatal management, which includes the planned
timing and the mode of delivery. Accurate prediction of weight using prenatal
ultrasound is challenging as it requires images of specific fetal body parts
during advanced pregnancy which is difficult to capture due to poor quality of
images caused by the lack of amniotic fluid. As a consequence, predictions
which rely on standard methods often suffer from significant errors. In this
paper we propose the Residual Transformer Module which extends a 3D
ResNet-based network for analysis of 2D+t spatio-temporal ultrasound video
scans. Our end-to-end method, called BabyNet, automatically predicts fetal
birth weight based on fetal ultrasound video scans. We evaluate BabyNet using a
dedicated clinical set comprising 225 2D fetal ultrasound videos of pregnancies
from 75 patients performed one day prior to delivery. Experimental results show
that BabyNet outperforms several state-of-the-art methods and estimates the
weight at birth with accuracy comparable to human experts. Furthermore,
combining estimates provided by human experts with those computed by BabyNet
yields the best results, outperforming either of other methods by a significant
margin. The source code of BabyNet is available at
https://github.com/SanoScience/BabyNet.Comment: Early accepted for 25th International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI) 2022, Singapor
Machine Learning Methods for Preterm Birth Prediction: A Review
Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future
Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings
: Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field