U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images
This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a s...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2021-04-01
|
| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/119624/PDF/05_02001_Bpast.No.69(3)_23.06.21_Druk.pdf |
| _version_ | 1811344009726001152 |
|---|---|
| author | Tomasz Les |
| author_facet | Tomasz Les |
| author_sort | Tomasz Les |
| collection | DOAJ |
| description | This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions. |
| first_indexed | 2024-04-13T19:40:12Z |
| format | Article |
| id | doaj.art-75541a55eab84eafaa3cf185bb2b28c2 |
| institution | Directory Open Access Journal |
| issn | 2300-1917 |
| language | English |
| last_indexed | 2024-04-13T19:40:12Z |
| publishDate | 2021-04-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| spelling | doaj.art-75541a55eab84eafaa3cf185bb2b28c22022-12-22T02:32:55ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172021-04-01693https://doi.org/10.24425/bpasts.2021.137051U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic imagesTomasz Les0Faculty of Electrical Engineering, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, PolandThis work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.https://journals.pan.pl/Content/119624/PDF/05_02001_Bpast.No.69(3)_23.06.21_Druk.pdfkidney detectionmedical image processingu-netframes partitioningvolumetric analysis |
| spellingShingle | Tomasz Les U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images Bulletin of the Polish Academy of Sciences: Technical Sciences kidney detection medical image processing u-net frames partitioning volumetric analysis |
| title | U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| title_full | U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| title_fullStr | U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| title_full_unstemmed | U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| title_short | U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| title_sort | u net based frames partitioning and volumetric analysis for kidney detection in tomographic images |
| topic | kidney detection medical image processing u-net frames partitioning volumetric analysis |
| url | https://journals.pan.pl/Content/119624/PDF/05_02001_Bpast.No.69(3)_23.06.21_Druk.pdf |
| work_keys_str_mv | AT tomaszles unetbasedframespartitioningandvolumetricanalysisforkidneydetectionintomographicimages |