Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

Casella, Alessandro; Moccia, Sara; Frontoni, Emanuele; Paladini, D.; Momi, Elena De; Mattos, Leonardo S.

doi:10.1007/s10439-019-02424-9

Cited by 23 publications

(36 citation statements)

References 36 publications

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“…We also would like to investigate more advanced models such as nnUNet [42], for improving the delineation performance. Adversarial training to take into account HC shape priors may also be explored [43], as well as atlas-based approaches [44]. As future work, we also plan to exploit synthetic augmentation techniques through generative adversarial networks.…”

Section: Discussionmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intraand inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 (± 1.76) mm and a Dice similarity coefficient of 97.75 (± 1.32) % were achieved, overcoming approaches in the literature. Conclusions The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.

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Section: Discussionmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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“…In this work, we address the problem of automatic inter-fetal membrane segmentation to enhance surgeon context awareness during TTTS surgery. Specifically, we extend the adversarial framework presented in Casella et al (2020) to process, via spatio-temporal convolution, surgical video clips. This allows us to exploit the temporal information naturally encoded in videos.…”

Section: Contribution Of the Workmentioning

confidence: 99%

“…The transition down and transition up modules perform downsampling and upsamplig, respectively. The critic, inspired by Casella et al (2020), consists of a 3D version of the encoder branch of UNet. During training, as explained in Sec.…”

Section: Introductionmentioning

confidence: 99%

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A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation

Casella

Moccia

Paladini

et al. 2021

Medical Image Analysis

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Background and Objectives During Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the fetuses. In the current practice, this syndrome is surgically treated by closing the abnormal connections using laser ablation. Surgeons commonly use the inter-fetal membrane as a reference. Limited field of view, low fetoscopic image quality and high inter-subject variability make the membrane identification a challenging task. However, currently available tools are not optimal for automatic membrane segmentation in fetoscopic videos, due to membrane texture homogeneity and high illumination variability.Methods To tackle these challenges, we present a new deep-learning framework for inter-fetal membrane segmentation on in-vivo fetoscopic videos. The framework enhances existing architectures by (i) encoding a novel (instancenormalized) dense block, invariant to illumination changes, that extracts spatiotemporal features to enforce pixel connectivity in time, and (ii) relying on an adversarial training, which constrains macro appearance. Results We performed

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“…However, extensive research shows that U-Net networks have certain limitations, so scholars have proposed different U-Net model variants. Casella et al [ 17 ] implemented an adversarial network consisting of two fully convolutional neural networks. One (segmenter) is a segmentation network inspired by U-Net and integrated with residual blocks.…”

Section: Introductionmentioning

confidence: 99%

An Ore Image Segmentation Method Based on RDU-Net Model

Xiao

Liu

et al. 2020

Sensors

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The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

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Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

Cited by 23 publications

References 36 publications

A regression framework to head-circumference delineation from US fetal images

A regression framework to head-circumference delineation from US fetal images

A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation

An Ore Image Segmentation Method Based on RDU-Net Model

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