Support system for pathologists and researchers

• Published in: Journal of pathology informatics Vol. 6; no. 1; p. 34
• Main Authors: Ishikawa, Takumi, Takahashi, Junko, Kasai, Mai, Shiina, Takayuki, Iijima, Yuka, Takemura, Hiroshi, Mizoguchi, Hiroshi, Kuwata, Takeshi
• Format: Journal Article
• Language: English
• Published: India Elsevier Inc 01.01.2015; Wolters Kluwer - Medknow Publications; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Auto-pathological diagnosis; Auto-pathological diagnosis, machine learning, OpenSlide library, virtual slide; Diagnostic imaging; Image processing; Innovations; machine learning; OpenSlide library; Original; Pathologists; Practice; virtual slide; OpenSlide library; virtual slide; Auto-pathological diagnosis; machine learning
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/2153-3539.158911

Aims: In Japan, cancer is the most prevalent cause of death; the number of patients suffering from cancer is increasing. Hence, there is an increased burden on pathologists to make diagnoses. To reduce pathologists’ burden, researchers have developed methods of auto-pathological diagnosis. However, virtual slides, which are created when glass slides are digitally scanned, saved in a unique format, and it is difficult for researchers to work on the virtual slides for developing their own image processing method. This paper presents the support system for pathologists and researchers who use auto-pathological diagnosis (P-SSD). Main purpose of P-SSD was to support both of pathologists and researchers. P-SSD consists of several sub-functions that make it easy not only for pathologists to screen pathological images, double-check their diagnoses, and reduce unimportant image data but also for researchers to develop and apply their original image-processing techniques to pathological images. Methods: We originally developed P-SSD to support both pathologists and researchers developing auto-pathological diagnoses systems. Current version of P-SSD consists of five main functions as follows: (i) Loading virtual slides, (ii) making a supervised database, (iii) learning image features, (iv) detecting cancerous areas, (v) displaying results of detection. Results: P-SSD reduces computer memory size random access memory utilization and the processing time required to divide the virtual slides into the smaller-size images compared with other similar software. The maximum observed reduction in computer memory size and reduction in processing time is 97% and 99.94%, respectively. Conclusions: Unlike other vendor-developed software, P-SSD has interoperability and is capable of handling virtual slides in several formats. Therefore, P-SSD can support both of pathologists and researchers, and has many potential applications in both pathological diagnosis and research area.