Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422235 | PMC |
| http://dx.doi.org/10.1007/s10278-017-9947-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prevalence and associations of cerebral microbleeds in an Australian memory clinic cohort.
Intern Med J
December 2024
Medical and Cognitive Research Unit, Department of Geriatric Medicine, Austin Health, Melbourne, Victoria, Australia.
Background: Cerebral microbleeds (CMBs) are small brain haemorrhages, identified by magnetic resonance imaging (MRI). They indicate potential for cognitive decline and mortality in memory clinic attendees. The presence of more than four CMBs is exclusionary for some clinical trials of disease-modifying therapies for Alzheimer's disease (AD).
View Article and Find Full Text PDFCardiovascular problems in rabbits in reference to hypothyroidism - a four-year retrospective study.
Pol J Vet Sci
December 2024
Department of Epizootiology and the Clinic of Infectious Diseases, Faculty of Veterinary Medicine, University of Life Sciences in Lublin, Głęboka 30, 20-612 Lublin, Poland.
The effects of T4 are mainly manifested by positive ino- and chronotropism. The syndrome accompanying hypothyroidism in rabbits (impaired myocardial contractility and reduced ejection capacity) is caused by a deficiency of thyroid hormones - especially T4. The study group consisted of a total of 41 animals: 15 males and 26 females, ranging in age from 2 months to 8 years, with echocardiogram showing reduced fractional shortening (<30%), with normal results of heamatological and biochemical tests.
View Article and Find Full Text PDFAiding Diagnosis and Classifying of Early Stage Osteonecrosis of the Femoral Head with Convolutional Neural Network Based on Magnetic Resonance Imaging.
Indian J Orthop
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China.
Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.
View Article and Find Full Text PDFDeep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.
Front Endocrinol (Lausanne)
December 2024
Department of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Objective: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.
Methods: Patients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!