Estimation of Preterm Birth Markers with U-Net Segmentation Network

Wlodarczyk, Tomasz Wiktor; Płotka, Szymon; Trzciński, Tomasz Piotr; Rokita, Przemyslaw Stefan; Sochacki‐Wójcicka, Nicole; Lipa, Michał; Wójcicki, J

doi:10.1007/978-3-030-32875-7_11

Cited by 15 publications

(9 citation statements)

References 11 publications

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“…Nerve segmentation [151], [222] Base U-net [50] Inception block [223] Residual block [224] Modified parallel U-net Breast lesion [225] Base U-net [39] Attention gate Arterial wall [226] Base U-net Cardiovascular structures [227] Base U-net Fetal head [219] Base U-net Gastrointestinal tumor [228] Base U-net Knee cartilage [96] Modified U-net with dual parallel encoders Preterm birth prediction [220] Base U-net Thyroid [229] Residual block Transcranial detection [221] Base U-net Ovary detection [230] Base U-net…”

Section: Reference Model/methods Usedmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Reference Model/methods Usedmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…In [165], a U-Net is used to segment the cervix from US images. The masks obtained are used to estimate the cervical length and the anterior cervical angle.…”

Section: Other Tasksmentioning

confidence: 99%

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Fiorentino¹,

Villani²,

Cosmo³

et al. 2022

Preprint

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Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite the large number of survey papers already present in this field, most of them are focusing on a broader area of medicalimage analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.

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“…While predictive machine learning (ML) models have enjoyed an ever-increasing popularity, preterm birth has only been addressed as a classification problem. Models based on electronic health records, uterine electromyography and transvaginal February 6, 2024 3/24 ultrasound [47] reported accuracies of approximately 0.77 [48][49][50], while studies based on electrohysterography reported values above 0.94 [51][52][53], with the latter, however, only including records of women with recorded contractile activity [53,54].…”

Section: Introductionmentioning

confidence: 99%

Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI

Fajardo-Rojas,

Hall,

Cromb

et al. 2024

Preprint

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Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal morphological and functional fetal MRI data from 176 control cases and 67 preterm birth cases. The GA at birth predictions were classified into term and preterm categories and their accuracy, sensitivity, and specificity were reported. An ablation study was performed to further validate the design of the pipeline. The pipeline achieves an R2 score of 0.51 and a mean absolute error of 2.22 weeks. It also achieves a 0.88 accuracy, 0.86 sensitivity, and 0.89 specificity, outperforming previous classification efforts in the literature. The predominant features selected by the pipeline include cervical length and various placental T2* values. The confluence of fast, motion-robust and multi-modal fetal MRI techniques and machine learning prediction allowed the prediction of the gestation at birth. This information is essential for any pregnancy. To the best of our knowledge, preterm birth had only been addressed as a classification problem in the literature. Therefore, this work provides a proof of concept. Future work will increase the cohort size to allow for finer stratification within the preterm birth cohort.

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Estimation of Preterm Birth Markers with U-Net Segmentation Network

Cited by 15 publications

References 11 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI

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