title: Support system for pathologists and researchers.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Takumi
authors.initials: T
authors.lastname: Ishikawa
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Junko
authors.initials: J
authors.lastname: Takahashi
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Mai
authors.initials: M
authors.lastname: Kasai
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Takayuki
authors.initials: T
authors.lastname: Shiina
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Yuka
authors.initials: Y
authors.lastname: Iijima
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Takemura
authors.affiliation: Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mizoguchi
authors.affiliation: Pathology Division, Cancer Center Hospital East, Chiba, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kuwata
keywords: Auto-pathological diagnosis; OpenSlide library; machine learning; virtual slide