Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing

Pnev, Sergey; Groza, Vladimir; Tuchinov, Bair; Amelina, Evgeniya; Pavlovskiy, Evgeniy; Tolstokulakov, Nikolay; Amelin, Mihail; Golushko, Sergey; Letyagin, A. Yu.

doi:10.1007/978-3-031-09002-8_24

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Выполнено сравнение эффектив- Разработанные и обученные нами модели ИИ с различной архитектурой (табл. 3) проверялись на тестовом наборе конкурса BraTS 2021, и усложнение архитектуры с добавлением блока самоконтроля (nn-Unet with self-supervised) в сочетании двух технологий (nn-Unet + DMFnet и nn-Unet with self-supervised + DMFnet) позволило достичь уровня Dice 0,9242 для сегментации всего объема опухоли [7]. Также в рамках задачи сегментации опухолей головного мозга разработан и протестирован 2D-подход -как на SBT Dataset, так и в рамках соревнований BraTS 2020 и BraTS 2021.…”

Section: результатыunclassified

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

Letyagin,

Tuchinov,

Amelina

et al. 2024

Sibirskij nauchnyj medicinskij zhurnal

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The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.”

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Section: результатыunclassified

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

Letyagin,

Tuchinov,

Amelina

et al. 2024

Sibirskij nauchnyj medicinskij zhurnal

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Software for brain tumor diagnosis on magnetic resonance imaging

Tuchinov

Letyagin

Амелина

et al. 2023

Digital Diagnostics

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BACKGROUND: The main reason for the development and implementation of artificial intelligence (AI) technologies in neuro-oncology is the high prevalence of brain tumors reaching up to 200 cases per 100,000 population. The incidence of a primary focus in the brain is 5%10%; however, 60%70% of those who die from malignant neoplasms have metastases in the brain. Magnetic resonance imaging (MRI) is the most common method for primary non-invasive diagnosis of brain tumors and monitoring disease progression. One of the challenges is the classification of tumor types and determination of clinical parameters (size and volume) for the conduct, diagnosis, and treatment procedures, including surgery. AIM: To develope a software module for the differential diagnosis of brain neoplasms on MRI images. METHODS: The software module is based on the developed Siberian Brain Tumor Dataset (SBT), which contains information on over 1000 neurosurgical patients with fully verified (histologically and immunohistochemically) postoperative diagnoses. The data for research and development was presented by the Federal Neurosurgical Center (Novosibirsk). The module uses two- and three-dimensional computer vision models with pre-processed MRI sequence data included in the following packages: pre-contrast T1-weighted image (WI), post-contrast T1-WI, T2-WI, and T2-WI with fluid-attenuated inversion-recovery technique. The models allow to detect and recognize with high accuracy 4 types of neoplasms, such as meningioma, neurinoma, glioblastoma, and astrocytoma, and segment and distinguish components and sizes: ET (tumor core absorbing Gd-containing contrast), TC (tumor core) = ET + Necr (necrosis) + NenTu, and WT (whole tumor) = TC + Ed (peritumoral edema). RESULTS: The developed software module shows high segmentation results on SBT by Dice metric for ET 0.846, TC 0.867, WT 0.9174, Sens 0.881, and Spec 1.000 areas. The testing and validation were done at the international BraTS Challenge 2021 competition. The test dataset yielded DiceET 0.86588, DiceTC 0.86932, and DiceWT 0.921 values, placing the developed software module in the top ten. According to the classification, the results demonstrate high accuracy rates of up to 92% in patient analysis (up to 89% in slice analysis), a very high potential, and a perspective for future research in this area. CONCLUSIONS: The developed software module may be used for training specialists and in clinical diagnostics.

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Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing

Cited by 2 publications

References 18 publications

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

Software for brain tumor diagnosis on magnetic resonance imaging

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