Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm

Umi Kalsom Mohamad Yusof; Syamsiah Mashohor; Marsyita Hanafi; Sabariah Md Noor; Norsafina Zainal

doi:10.1016/j.dib.2023.109484

Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm

Data Brief. 2023 Aug 11:50:109484. doi: 10.1016/j.dib.2023.109484. eCollection 2023 Oct.

Authors

Umi Kalsom Mohamad Yusof¹, Syamsiah Mashohor¹, Marsyita Hanafi¹, Sabariah Md Noor², Norsafina Zainal³

Affiliations

¹ Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
² Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
³ Pathology Department, Hospital Serdang, Jalan Puchong, 43000 Kajang, Selangor, Malaysia.

Abstract

Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. This paper reports a histopathological imagery dataset that was created to focus on the most common MPN from the Philadelphia Chromosome (Ph)-negative type, namely Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (MF). The dataset consisted of 300 BMT images that can be used to enable computer vision applications, such as image segmentation, disease classification, and object recognition, in assisting the classification of the MPN disease. Ethical approval was obtained from the Ministry of Health, Malaysia and the bone marrow trephine images were captured using a digital microscope from the Olympus model (BX41 Dual head microscope) with x10, x20, and x40 lens types. The development of comprehensive tools deployed from this dataset can assist medical practitioners in diagnosing diseases, thus overcoming the current challenges.

Keywords: Artificial intelligence; Essential thrombocythemia; Machine learning; Myeloproliferative neoplasm; Polycythaemia vera; Primary myelofibrosis.