Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery

Sadda, Praneeth; İmamoğlu, Metehan; Dombrowski, Michael; Papademetris, Xenophon; Bahtiyar, Mert Ozan; Onofrey, John A.

doi:10.1007/s11548-018-1886-4

Cited by 23 publications

(34 citation statements)

References 22 publications

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“…The proposed approach may also be integrated with recent work, which deals with vessel segmentation from placenta images 1,34 , stitching of fetoscopy images to build placental panoramic image 12,44 and classification of TTTS surgical phases 38 .…”

Section: Discussionmentioning

confidence: 99%

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

et al. 2019

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Twin-to-Twin Transfusion Syndrome (TTTS) is commonly treated with minimally invasive laser surgery in fetoscopy. The inter-foetal membrane is used as a reference to find abnormal anastomoses. Membrane identification is a challenging task due to small field of view of the camera, presence of amniotic liquid, foetus movement, illumination changes and noise. This paper aims at providing automatic and fast membrane segmentation in fetoscopic images. We implemented an adversarial network consisting of two Fully-Convolutional Neural Networks (FCNNs). The former (the segmentor) is a segmentation network inspired by U-Net and integrated with residual blocks, whereas the latter acts as critic and is made only of the encoding path of the segmentor. A dataset of 900 images acquired in 6 surgical cases was collected and labelled to validate the proposed approach. The adversarial networks achieved a median Dice similarity coefficient of 91.91% with Inter-Quartile Range (IQR) of 4.63%, overcoming approaches based on U-Net (82.98%-IQR : 14.41%) and U-Net with residual blocks (86.13%-IQR : 13.63%). Results proved that the proposed architecture could be a valuable and robust solution to assist surgeons in providing membrane identification while performing fetoscopic surgery.

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Section: Discussionmentioning

confidence: 99%

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

et al. 2019

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“…Convolution neural networks (e.g. U-Net) have been successfully applied in segmenting various vascular systems including retina [18, 19], brain [20], whole placental volume [21], and larger placental vessels [22]. Here for the first time, we apply a U-Net towards multi-domain segmentation of the intricate fetal vascular network and maternal porous space, including terminal capillary loops where vessel diameters are smaller than 50 μm.…”

Section: Discussionmentioning

confidence: 99%

“…Although the potential usefulness of U-Net in placental vessel segmentation has been shown in a previous study (21), only a small number of larger vessels were segmented, and the algorithm applied was relatively primitive. Here for the first time, we apply a U-Net towards multi-domain segmentation of the intricate fetal vascular network and maternal porous space, including terminal capillary loops, where vessel diameters are smaller than 50 μm.…”

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confidence: 99%

A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta

Tun

Poologasundarampillai

Bischof

et al. 2020

Preprint

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Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure-function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging, and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure-function relationships in both normal and diseased placentas, allowing us to optimise therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in characterisation of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.SummaryUsing the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced 3D X-ray imaging, and robust, validated machine-learning segmentation techniques to provide massively multi-scale and multi-domain insights on vascular-rich organ morphology and function.

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“…Moreover, FetNet can provide additional context for navigation and mapping algorithms using the fetoscopic camera and can help in generating better mosaics from fetoscopic videos [2] by focusing only on frames with occlusion-free views. FetNet can also assist in designing in vivo fetoscopic vessel segmentation [19] strategies by initially focusing only on occlusion-free frames. Additionally, vessel segmented prediction masks can be utilised for generating vascular mosaics (an expanded FoV image of the placental vascular structure) which may support the identification of abnormal vessels during the TTTS laser therapy.…”

Section: Fetnet_dlmentioning

confidence: 99%

FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

et al. 2020

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Purpose Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos. Methods We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. Results We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. Conclusion FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures.

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Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery

Cited by 23 publications

References 22 publications

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks

A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta

FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

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