Multi-class Brain Tumor Segmentation via Multi-sequences MRI Mixture Data Preprocessing

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

In this paper, we extend the previous work on the robust pre-processing technique which allows to consider all available information from MRI scans by composition of T1, T1C and FLAIR sequences in the unique input. Such approach enriches the input data for the automatic segmentation process and helps to improve the accuracy of the segmentation performance. Proposed method also demonstrate significant improvement on the multi-class segmentation problem with respect to Dice metrics compare to similar training / evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrate significant evaluation improvement while combining three MRI sequences in the 3-channel RGB like image for considered problem of multi-class brain tumor segmentation. We also provide results of comparison of various gradient descent optimization methods and of different backbone architectures. We found that different algorithms worked best for different tumors, but no single algorithm ranked in the top for all types of tumors simultaneously. Final improvements on the test part of our dataset are in the range of 6 - 9% on the trained model according to the Dice metric with the best value of 0.949.

Язык оригиналаанглийский
Название основной публикацииProceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020
Подзаголовок основной публикацииInternational symposium will take place in the frame of 12th International Multiconference “Bioinformatics of Genome Regulation and Structure/Systems Biology”
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы185-189
Число страниц5
ISBN (электронное издание)9781728195971
DOI
СостояниеОпубликовано - июл 2020
Событие2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 - Novosibirsk, Российская Федерация
Продолжительность: 6 июл 202010 июл 2020

Серия публикаций

НазваниеProceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Конференция

Конференция2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020
СтранаРоссийская Федерация
ГородNovosibirsk
Период06.07.202010.07.2020

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