Multiclass U-Net Segmentation of Brain Electron Microscopy Data Using Original and Semi-Synthetic Training Datasets

Getmanskaya, Alexandra; Sokolov, N. A.; Турлапов, Вадим

doi:10.1134/s0361768822030057

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Исследования, представленные в этой статье, являются значительным развитием нашей работы [15], мы расширяем тему генерации синтетических изображений, улучшая генерацию некоторых органелл и добавляя возможность параметризации алгоритмов генерации синтетических изображений для достижения высокого сходства с разными реальными наборами данных с помощью единого универсального кода.…”

Section: рисунок 4 -пример синтетических изображений разных органеллunclassified

“…В предыдущей статье [15] мы представили первые результаты создания алгоритма, синтезирующего ЭМ-данные и разметку. Мы использовали синтетические данные, чтобы дополнить исходный набор данных синтетическим аксоном.…”

Section: Introductionunclassified

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Generation and Study of the Synthetic Brain Electron Microscopy Dataset for Segmentation Purpose

Sokolov

Vasiliev

Getmanskaya

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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Advanced microscopy technologies such as electron microscopy have opened up a new field of vision for biomedical researchers. The use of artificial intelligence methods for processing EM data is largely difficult due to the small amount of annotated data at the training stage. Therefore, we add synthetic images to an annotated real EM dataset or use a fully synthetic training dataset. In this work, we present an algorithm for the synthesis of 6 types of organelles. Based on the EPFL dataset, a training set of 860 real fragments 256x256 (ORG) and 6000 synthetic ones (SYN), as well as their combination (MIX), were generated. An experiment of training models for segmentation into 5 and 6 classes showed that, despite the imperfection of synthetic data, for an axon poorly represented in the training data set, the use of a synthetic data set improves the Dice metric from 0.3 on the original dataset to 0.8 on the mixed and synthetic datasets. The synthetic data strategy gives annotations for free, but shifts the effort to producing sufficiently realistic images.

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Section: рисунок 4 -пример синтетических изображений разных органеллunclassified

Section: Introductionunclassified

Generation and Study of the Synthetic Brain Electron Microscopy Dataset for Segmentation Purpose

Sokolov

Vasiliev

Getmanskaya

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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“…In our previous article [15], we presented the first results of creating of an algorithm that synthesizes EMdata and markup. We used synthetic data to supplement the original dataset with a synthetic axon.…”

Section: Introductionmentioning

confidence: 99%

Generation and study of the synthetic brain electron microscopy dataset for segmentation purpose

Sokolov,

Vasiliev,

Getmanskaya

2023

Computer Optics

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Advanced microscopy technologies such as electron microscopy have opened up a new field of vision for biomedical researchers. The use of artificial intelligence methods for processing EM data is largely difficult due to the small amount of annotated data at the training stage. Therefore, we add synthetic images to an annotated real EM dataset or use a fully synthetic training dataset. In this work, we present an algorithm for the synthesis of 6 types of organelles. Based on the EPFL dataset, a training set of 1161 real fragments 256×256 (ORG) and 2000 synthetic ones (SYN), as well as their combination (MIX), were generated. The experiment of training models for 6, 5-classes and binary segmentation showed that, despite the imperfections of synthetics, training on a mixed (MIX) dataset gave a significant increase (about 0.1) in the Dice metric for 6 and 5 and same results at binary. The synthetic data strategy gives annotations for free, but shifts the effort to producing sufficiently realistic images.

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Automated Method for Optimum Scale Search when Using Trained Models for Histological Image Analysis

Penkin

Khvostikov

Krylov

2023

Program Comput Soft

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Multiclass U-Net Segmentation of Brain Electron Microscopy Data Using Original and Semi-Synthetic Training Datasets

Cited by 4 publications

References 26 publications

Generation and Study of the Synthetic Brain Electron Microscopy Dataset for Segmentation Purpose

Generation and Study of the Synthetic Brain Electron Microscopy Dataset for Segmentation Purpose

Generation and study of the synthetic brain electron microscopy dataset for segmentation purpose

Automated Method for Optimum Scale Search when Using Trained Models for Histological Image Analysis

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