Medical Images Segmentation Operations
Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Ivannikov Institute for System Programming of the Russian Academy of Sciences
2018-10-01
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| Series: | Труды Института системного программирования РАН |
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| Online Access: | https://ispranproceedings.elpub.ru/jour/article/view/563 |
| _version_ | 1818261408231456768 |
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| author | S. A. Musatian A. V. Lomakin S. Yu. Sartasov L. K. Popyvanov I. B. Monakhov A. S. Chizhova |
| author_facet | S. A. Musatian A. V. Lomakin S. Yu. Sartasov L. K. Popyvanov I. B. Monakhov A. S. Chizhova |
| author_sort | S. A. Musatian |
| collection | DOAJ |
| description | Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas. |
| first_indexed | 2024-12-12T18:46:45Z |
| format | Article |
| id | doaj.art-f485477cea1d40d395c608740837bc32 |
| institution | Directory Open Access Journal |
| issn | 2079-8156 2220-6426 |
| language | English |
| last_indexed | 2024-12-12T18:46:45Z |
| publishDate | 2018-10-01 |
| publisher | Ivannikov Institute for System Programming of the Russian Academy of Sciences |
| record_format | Article |
| series | Труды Института системного программирования РАН |
| spelling | doaj.art-f485477cea1d40d395c608740837bc322022-12-22T00:15:30ZengIvannikov Institute for System Programming of the Russian Academy of SciencesТруды Института системного программирования РАН2079-81562220-64262018-10-0130418319410.15514/ISPRAS-2018-30(4)-12563Medical Images Segmentation OperationsS. A. Musatian0A. V. Lomakin1S. Yu. Sartasov2L. K. Popyvanov3I. B. Monakhov4A. S. Chizhova5Санкт-Петербургский государственный университетСанкт-Петербургский государственный университетСанкт-Петербургский государственный университетСанкт-Петербургский государственный университетСанкт-Петербургский государственный университетСанкт-Петербургский государственный университетExtracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.https://ispranproceedings.elpub.ru/jour/article/view/563глубокие нейронные сетисвёрточные нейронные сетиопухоли мозгакостные глазные орбитымедицинские изображения |
| spellingShingle | S. A. Musatian A. V. Lomakin S. Yu. Sartasov L. K. Popyvanov I. B. Monakhov A. S. Chizhova Medical Images Segmentation Operations Труды Института системного программирования РАН глубокие нейронные сети свёрточные нейронные сети опухоли мозга костные глазные орбиты медицинские изображения |
| title | Medical Images Segmentation Operations |
| title_full | Medical Images Segmentation Operations |
| title_fullStr | Medical Images Segmentation Operations |
| title_full_unstemmed | Medical Images Segmentation Operations |
| title_short | Medical Images Segmentation Operations |
| title_sort | medical images segmentation operations |
| topic | глубокие нейронные сети свёрточные нейронные сети опухоли мозга костные глазные орбиты медицинские изображения |
| url | https://ispranproceedings.elpub.ru/jour/article/view/563 |
| work_keys_str_mv | AT samusatian medicalimagessegmentationoperations AT avlomakin medicalimagessegmentationoperations AT syusartasov medicalimagessegmentationoperations AT lkpopyvanov medicalimagessegmentationoperations AT ibmonakhov medicalimagessegmentationoperations AT aschizhova medicalimagessegmentationoperations |