Research project and development of a computer-aided diagnosis system prototype that provided anatomic pathologists with a valid support in the diagnosis of the carcinoma of the cervix. Based on advanced machine learning and high performance computing algorithms on large images taken in the Papanicolaou (Pap) test.

1. MIACell Project
Medical Image Parallel Processing with Cell Broadband Engine
Project Background
Prior to the introduction of the Papanicolaou (Pap) test, carcinoma of the cervix was a leading cause of death in
women. Since its introduction deaths caused by that pathology have been sharply reduced in populations where
women undergo regular and widespread screening.
The Pap test involves taking a sample of cells from the uterine cervix and transferring it onto a glass slide that is
later screened by a cytotechnologist using a microscope. Traditional screening process is time-consuming
(analyzing a slide by an expert doctor takes typically more than half an hour), error-prone and negatively affected
by human factors such as eyestrain.
In recent years digitalization is revolutionizing the medicine. In fact the cost reduction both in devices such as
microscopes and in image databases is leading toward mass digitalization. In addition, modern digital microscopes
reach resolutions higher than 100.000 dots per inch (dpi), typical of optical scopes.
Unfortunately a digital image taken from a Pap Test smear is huge: by using a 20x magnification factor (about
54000 dpi) its dimension is about 2.8GB uncompressed. Scanning a 15mmx15mm slide at 20x in modern digital
microscopes takes about two minutes. Assuming to use a digital scope with 120-slide autoload capacity and load
three slides packs a day approximately a 1TB of images would be produced per day. Till now limited performance
of available computing architectures has often led to exploit approximated algorithms for image processing (thus
reducing the effectiveness) or to use highly specialized and expensive machines
Project Description
The IBM Innovation Technology Center based in Cagliari has led a project, named MIACell, in collaboration with
Italian research institutes (CRS4, Università degli Studi di Bologna - Spin Off ARCADIA LAB) and clinics, aimed
at developing a computer-aided diagnosis system that provides anatomic pathologists with a valid diagnostic
support.
CAD System prototype
The system prototype is dubbed PapCAD. The PapCAD uses high resolution (typically 2160 megapixels) images
of pap smears. After processing the image, it automatically classifies it as normal or pathologic and marks suspect
zones in the image for further review by a pathologist. The current prototype takes a few minutes for the entire
elaboration and there is room for further improvement.
PapCADPapCAD
Figure 1 High-level system overview

2. Algorithms and Technology
With advanced machine learning and pattern recognition algorithms, the system self-learns analyzing and
comparing several images. The more it is trained the more its internal model is accurate.
The problem is computationally demanding since many huge images have to be analyzed. The PapCAD approaches
this task by adopting a hybrid solution, based on both traditional SMPs and IBM Cell Broadband Engine
accelerators for some highly computing power demanding tasks. What’s more, in order to reduce development and
developer learning times, the software cross-platform development process was inspired by agile programming
principles and state-of-the-art parallelizing and auto-vectorizing compilers were leveraged to achieve rapidly good
system performance while keeping the code portable and at a high level.
Pathologists Benefits
In conclusion, the PapCAD is not meant to replace the pathologists but to help them to improve their diagnosis
accuracy and therefore improve treatment of patients.
Some benefits derive from digitalizing the analysis process. Doctors, even remotely, can navigate the image like a
map and search for pathologic cells using a PC. The images can be archived into a database, which has great value
for the entire scientific community.
The PapCAD provides the doctor automatically with a list of likely pathologic zones and mark them in the image
for review (see Figure 2).
Using the PapCAD a low experienced pathologist can reach the accuracy of a more expert colleague and an expert
pathologist can save a lot of time and improve his productivity.
The reach of this technology is not limited to pap tests, but it can be exploited for different biologic images. For
example, the same technology has been successfully deployed for digital mammography.
Contacts
Andrea Corona
IBM Cagliari Innovation Technical Center - Italy
andrea.corona@it.ibm.com
Robert Alexander
IBM Systems & Technology Group - Italy
rja@it.ibm.com
Figure 2 Pap smear image after PapCAD processing