| Full text | |
| Author(s): |
Cazzolato, Mirela T.
[1]
;
Ramos, Jonathan S.
[1]
;
Rodrigues, Lucas S.
[1]
;
Scabora, Lucas C.
[1]
;
Chino, Daniel Y. T.
[1, 2, 3]
;
Jorge, Ana E. S.
;
de Azevedo-Marques, Paulo Mazzoncini
[4]
;
Jr, Caetano Traina
;
Traina, Agma J. M.
[5]
Total Authors: 9
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo, SP - Brazil
[2] InterlockLedger, Sao Paulo, SP - Brazil
[3] Fed Univ Sao Carlos UFSCar, Dept Phys Therapy, Sao Paulo, SP - Brazil
[4] Univ Sao Paulo, Ribeirao Preto Med Sch, Ribeirao Preto - Brazil
[5] Jr, Jr., Caetano Traina, Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo, SP - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | COMPUTERS IN BIOLOGY AND MEDICINE; v. 134, JUL 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Chronic dermatological ulcers cause great discomfort to patients, and while monitoring the size of wounds over time provides significant clues about the healing evolution and the clinical condition of patients, the lack of practical applications in existing studies impairs users' access to appropriate treatment and diagnosis methods. We propose the UTrack framework to help with the acquisition of photos, the segmentation and measurement of wounds, the storage of photos and symptoms, and the visualization of the evolution of ulcer healing. UTrack-App is a mobile app for the framework, which processes images taken by standard mobile device cameras without specialized equipment and stores all data locally. The user manually delineates the regions of the wound and the measurement object, and the tool uses the proposed UTrack-Seg segmentation method to segment them. UTrackApp also allows users to manually input a unit of measurement (centimeter or inch) in the image to improve the wound area estimation. Experiments show that UTrack-Seg outperforms its state-of-the-art competitors in ulcer segmentation tasks, improving F-Measure by up to 82.5% when compared to superpixel-based approaches and up to 19% when compared to Deep Learning ones. The method is unsupervised, and it semi-automatically segments real-world images with 0.9 of F-Measure, on average. The automatic measurement outperformed the manual process in three out of five different rulers. UTrack-App takes at most 30 s to perform all evaluation steps over high-resolution images, thus being well-suited to analyze ulcers using standard mobile devices. (AU) | |
| FAPESP's process: | 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/17330-1 - Storage and Navigation Operations on Graphs in Relational DBMS |
| Grantee: | Lucas de Carvalho Scabora |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 20/11258-2 - Interoperability and similarity queries on medical databases |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 20/10902-5 - Handling similarity queries over incomplete data in a Relational DBMS |
| Grantee: | Lucas Santiago Rodrigues |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |