A time-lapse embryo dataset for morphokinetic parameter prediction

Gomez, Tristan; Feyeux, Magalie; Boulant, Justine; Normand, Nicolas; David, Laurent; Paul‐Gilloteaux, Perrine; Fréour, Thomas; Mouchère, Harold

doi:10.1016/j.dib.2022.108258

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Lastly, the recent appearance of data collections with an evolutive point of view, as the one developed by Gomez et al [ 181 ], proves that generative models could not only focus on a single image, but also to cover different states through a period of time. This process is similar to the generation of videos and sequences of images through LSTM blocks and 3D Convolutions, as proposed in [ 103 ], but further research could develop this field and offer great benefits in the medical field.…”

Section: Discussionmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

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Section: Discussionmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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“…Embryo Development is a public time-lapse dataset created by Gomez et al [47] of the culture of embryos that have undergone IVF. It consists of 704 videos in different focal planes for a total of 2.4 million images with a resolution of 500 × 500.…”

Section: Embryo Datasetmentioning

confidence: 99%

Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

Celard,

Seara Vieira,

Sorribes-Fdez

et al. 2023

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In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.

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Comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study

Gomez

Fréour

Mouchère

2023

Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges

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A time-lapse embryo dataset for morphokinetic parameter prediction

Cited by 9 publications

References 6 publications

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

Comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study

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