Введение 3
Глава 1. Обзор литературы 3
Глава 2. Постановка задачи 4
Глава 3. Материал и методы 5
3.1. Пациенты 5
3.2. Получение данных 5
3.3. Методы 5
3.3.1 Суперпиксельный метод 5
3.3.2 Ленивый метод обрезки 6
3.4. статистический анализ 8
Глава 4. Фон 8
Глава 5. Полученные результаты 9
Вывод 11
Список литературы 11
Оценка прогрессирования заболевания очень важна в медицине, что помогает точно проанализировать тип заболевания. Например, при заболевании COVID-19 одним из критических компонентов для каждого пациента является проверка уровня прогрессирования вируса и инфекции в легких, в соответствии с которым должно определяться лечение пациента. Получение медиками уровня прогрессирования заболевания является сложной задачей и требует много времени, тогда как диагностика и лечение пациентов с COVID-19 требуют скоростных методов. Это связано с быстрым распространением заболевания, а в ряде случаев и с отсутствием свободных коек в больницах, что усложняет лечение и требует большей оперативности. [1].
В этой статье были рассмотрены стандартные методы разделения раз-личных тканей легких на изображениях компьютерной томографии по категориям. Также был предложен новый метод выделения инфекций, вызванных COVID-19, в легком с использованием изображений компьютерной томографии, позволяющих с высокой точностью сегментировать инфицированную ткань. Этот метод основан на алгоритмах Lazy-Snipping и Super-pixel, которые имеют множество применений для сегментации узелков в легких.
В этой работе мы представляем полуавтоматический метод выделения инфекций, вызванных COVID-19, на КТ-изображениях. Наш метод может улучшить результаты сегментации, даже если у нас нет достаточной достоверности сегментации. Наш метод показывает очень многообещающие результаты и может оказать большую помощь врачам в лабораториях.
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