|
[[Image:Microscope with stained slide.jpg|thumb|A stained histologic specimen, sandwiched between a glass [[microscope slide]] and coverslip, mounted on the stage of a light microscope.]]
[[Image:Emphysema H and E.jpg|thumb|Microscopic view of a histologic specimen of human [[lung]] tissue stained with [[hematoxylin]] and [[eosin]].]]
'''Automated tissue image analysis''' or '''histopathology image analysis''' ('''HIMA''') is a process by which computer-controlled [[automatic test equipment]] is used to evaluate [[tissue (biology)|tissue]] samples, using computations to derive quantitative measurements from an image to avoid subjective errors.
In a typical application, automated tissue image analysis could be used to measure the aggregate activity of [[cancer cell]]s in a [[biopsy]] of a [[cancer]]ous [[tumor]] taken from a patient. In [[breast cancer]] patients, for example, automated tissue image analysis may be used to test for high levels of [[proteins]] known to be present in more aggressive forms of breast cancers.
=== WHAT IS THE AUTOMATIC TISSUE PROCESSOR MACHINE (ATPM)? ===
A tissue processor is a device that prepares tissue samples for sectioning and microscopic examination in the diagnostic laboratory.
Source
Microscopic analysis of cells and tissues requires the preparation of very thin, high quality sections (slices) mounted on glass slides and appropriately stained to demonstrate normal and abnormal structures.
The ATP machine plays a big role in the preparation of the tissue by passing them through various chemicals; a major process called TISSUE PROCESSING.
<br />
= THE AUTOMATIC TISSUE PROCESSOR MACHINE =
= INTRODUCTION =
Machines are devices that makes our work easier for us and it does it so efficiently.
• Imagine what the world will be like without machines. Now, machines have found their way into our laboratories, as they help to improve standard health practices.
One of such machine is the –AUTOMATIC TISSUE PROCESSOR. A machine routinely used in the histopathology laboratory.
=== WHAT IS THE AUTOMATIC TISSUE PROCESSOR MACHINE (ATPM)? ===
A tissue processor is a device that prepares tissue samples for sectioning and microscopic examination in the diagnostic laboratory.
Source
Microscopic analysis of cells and tissues requires the preparation of very thin, high quality sections (slices) mounted on glass slides and appropriately stained to demonstrate normal and abnormal structures.
The ATP machine plays a big role in the preparation of the tissue by passing them through various chemicals; a major process called TISSUE PROCESSING
=== BRIEF HISTORY OF TISSUE PROCESSOR ===
Tissue processing has been in existence as far back as in the late 18th century. Major breakthroughs about the basic components of tissue were made possible because of tissue processor.
Disease investigation have also been made easy by subjecting tiny piece (biopsy) of the tissue or organ to some special chemical treatments.
Tissue processing was done manually in the 18th and 19th century. A cruel long process that took days and sleepless nights to achieve this feat. This discomfort forced scientists into looking for a better and a more efficient way to process tissues.
The first automatic tissue processors were introduced during the first half of the 20 th century. In the USA, they were produced under the name of Auto-Technicon and in the UK under the name of Histokine, and later by other companies.
These devices have slowly evolved to be safer to use, handle larger specimen numbers, process more quickly and to produce better quality outcomes.
=== TISSUE PROCESSING OVERVIEW ===
The ATPM works by following through an already established processing steps.
Tissues to be processed are cut into small pieces to ensure the tissue fits into the tissue cassettes
Source
Smaller tissues (2-4 um) will be processed faster than the whole tissue or organ.
These tissue cassettes are packed into the oscillating tissue basket to tissue prior to fixation.
Source<center>Pic of stainless tissue basket<br /></center>(i) FIXATION – this is the process of preserving or fixing tissues by passing them through chemicals called fixatives. The fixatives will help protect the tissue from decay and autolysis. Routine fixative of use is 10% formalin<br />(ii) DEHYDRATION – this is the process of removing water molecules from the tissue by passing the tissue through ascending grades of alcohol. E.g methanol, acetone, 70-100% alcohol<br />(iii) CLEARING – this is the process of removing alcohol from the tissue by passing it through chemicals that will remove the alcohol molecules. These agents are called clearing agents. Xylene is mostly used for clearing.<br />(iv) INFILTRATION – this is the process of filling intracellular spaces left in the tissue by paraffin wax. This will help confer a bit of rigidity to the processed tissue.<br />(v) EMBEDDING- this last step is manually done. This has to do with immersing the processed tissue into a mould containing liquid paraffin wax. This is for external support so that the tissue won’t crumble during microtomy<br />
==== PARTS OF THE ATPM – using TP 1050 Leica model ====
(i) Oscillating tissue basket
Source
(i) 10 beakers or jars
Source<center>The transparent beakers is for processing fluids<br /></center>(ii) 2 thermostatically controlled beakers<br />Source<br /><center>The coated beaker is for parrafin wax<br /></center>(iii) An electric rotor at the base
(iv) Lifting mechanism
(v) Time disc and alarm system
(vii) Control unit - with display screen and control buttons
Source<br />
==== WORKING PRINCIPLE OF AUTOMATIC TISSUE PROCESSOR MACHINE – TP 1050 Leica processor model ====
Most ATPMs are easy-to-program interface. The Leica processor model has ten 1.8L (60.9oz.) reagent beakers and two 1.8L (60.9oz.) wax baths.
The tissue basket oscillates up and down in each station at three-second intervals to ensure thorough and even mixing of the reagents and optimum tissue infiltration.
Infiltration time is separately programmable for each station. Up to nine programs may be run with immediate or delayed starting times.
When it’s time for tissue to be transferred to the next beaker or jar, the cover of the machine is raised up, and the lifting mechanism carefully removes the tissue basket and gently transfers it to the next beaker.
Source
When the infiltration time for any particular station is exceeded, a warning message will display, indicating the station number and excess time. Controls are arranged by functionality with an LCD to indicate operational parameters. Reagent container lids have seals to minimize operator exposure to hazardous fumes.
Tissue basket immediately immerses in a station in the event of power loss to protect samples from drying out. When power is restored, program will resume. In the event of long-term power failure, wax is liquified. Capacity of tissue basket is 80 cassettes.
Vacuum configurations hasten infiltration, allowing pressure to be applied to any station in either manual or automatic operation. Fume control configurations extract fumes with a fan and pass them through an internal carbon filter.
For added efficiency, these models feature a two-part containment shield surrounding the reagent container platform.
==== ATPM – processing time schedule ====
Processing schedule varies and it depends oh the following:
(i) Nature and size of tissue
(ii) Urgency
Beaker I – fixative (formalin) 1-2 hours
Beaker II – fixative 1 hour
Beaker III – fixative. 30- 45 minutes
Beaker IV – 70% alcohol. 30 minutes
Beaker V – 90% alcohol. 30 minutes
Beaker VI – Absolute alcohol. 1 hour
Beaker VII – Absolute alcohol. 1 hour
Beaker VIII – Methanol 30 minutes
Beaker IX – Xylene. 1-2 hours
Beaker X – Xylene 45 minutes – 1 hour
Wax bath I (done at 45°
<br /><br />
==Applications==
Automated tissue imaging analysis can significantly reduce uncertainty in characterizing [[tumors]] compared to evaluations done by [[histologist]]s,<ref name="O'Gorman_1985">
{{cite journal
|title=A System for Automated Liver Tissue Image Analysis: Methods and Results
|author1=O'Gorman, Lawrence
|author2=Sanderson, Arthur C.
|author3=Preston, Kendall
|journal=IEEE Transactions on Biomedical Engineering
|date=September 1985
|volume=BME-32
|issn=0018-9294
|doi=10.1109/TBME.1985.325587
|pmid=4054933
|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4122137
|s2cid=30050996
}}</ref> or improve the prediction rate of recurrence of some cancers.<ref name="Teverovskiy_2004">
}}</ref> or improve the prediction rate of recurrence of some cancers.<ref name="Teverovskiy_2004">
{{cite journal
{{cite book
|title=Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system
|author=Teverovskiy, M.
|author2= Kumar, V.
|author3= Junshui Ma
|author4= Kotsianti, A.
|author5= Verbel, D.
|author6= Tabesh, A.
|author7= Ho-Yuen Pang
|author8= Vengrenyuk, Y.
|author9= Fogarasi, S.
|author10= Saidi, O.
|author11= ((Aureon Biosciences Corp., Yonkers, NY, USA))
|journal=Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium
|date=2004-04-18
|pages=257–260
|volume=1
|isbn=0-7803-8388-5
|doi=10.1109/ISBI.2004.1398523
|citeseerx=10.1.1.58.9929
|chapter=Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system
|title=2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)
|volume=2
|pages=257–260
|year=2004
|last1=Teverovskiy |first1=M.
|last2=Kumar |first2=V.
|last3=Junshui Ma
|last4=Kotsianti |first4=A.
|last5=Verbel |first5=D.
|last6=Tabesh |first6=A.
|last7=Ho-Yuen Pang
|last8=Vengrenyuk |first8=Y.
|last9=Fogarasi |first9=S.
|last10=Saidi |first10=O.
|s2cid=8724168
}}</ref><ref>{{cite journal
|journal = IEEE Transactions on Medical Imaging
|volume = 26
|issue = 10
|date = October 2007
|doi = 10.1109/TMI.2007.898536
|pmid = 17948727
|issn = 0278-0062
|pages = 1366–1378
|title = Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images
|author1 = Ali Tabesh
|author2 = Mikhail Teverovskiy
|author3 = Ho-Yuen Pang
|author4 = Vinay P. Kumar
|author5 = David Verbel
|author6 = Angeliki Kotsianti
|author7 = Olivier Saidi
|s2cid=14673541
|url = http://claymore.rfmh.org/~atabesh/papers/tabesh_tmi07_final.pdf
|url=http://claymore.rfmh.org/~atabesh/papers/tabesh_tmi07_final.pdf
|access-date = 2010-09-04
|access-date=2010-09-04
|archive-url = https://web.archive.org/web/20110727214729/http://claymore.rfmh.org/~atabesh/papers/tabesh_tmi07_final.pdf
|archive-url=https://web.archive.org/web/20110727214729/http://claymore.rfmh.org/~atabesh/papers/tabesh_tmi07_final.pdf
|archive-date = 2011-07-27
|archive-date=2011-07-27
|dead-url = yes
|url-status=dead
|df =
}}</ref> As it is a digital system, suitable for networking, it also facilitates cooperative efforts between distant sites.<ref>
{{cite journal
|pages=49–58
|doi=10.1080/01926230490451734
|pmid=15503664
|url=http://tpx.sagepub.com/content/32/2_suppl/49
|doi-access=free}}</ref> Systems for automatically analyzing tissue samples also reduce costs and save time.<ref name="O'Gorman_1985"/>
High-performance [[Charge-coupled device|CCD cameras]] are used for acquiring the digital images. Coupled with advanced [[fluorescence microscope|widefield microscope]]s and various [[algorithms]] for [[Deconvolution#Optics and other imaging|image restoration]], this approach can provide better results than [[confocal microscope|confocal techniques]] at comparable speeds and lower costs.<ref name="Phukpattaranont_2007">
{{cite journalbook
|journal=IFMBE Proceedings
|year=2007
|volume=15
|doi=10.1007/978-3-540-68017-8_63
|title=An Automatic Cell Counting Method for a Microscopic Tissue Image from Breast Cancer
|author1=Pornchai Phukpattaranont |author2=Pleumjit Boonyaphiphat |urlseries=http://www.springerlink.com/content/v46774v3124w764r/IFMBE Proceedings
|series=IFMBE Proceedings
|isbn=978-3-540-68016-1}}</ref>
==Processes==
The [[United States of America|United States]] [[Food and Drug Administration]] classifies these systems as [[medical device]]s, under the general instrumentation category of [[automatic test equipment]].<ref>{{cite book|url=https://books.google.com/books?id=nEHLxh9wxZIC&pg=PA80&lpg=PA80&dq=%22FDA+automatic+test+equipment%22&pg=PA80 |title=Testing Computer Systems for FDA/MHRA Compliance - David Stokes - Google Books |publisher=Books.google.com |date= 2003-11-25|accessdate=2012-07-12|isbn=9780849321634|last1=Stokes |first1=David |publisher=Taylor & Francis }}</ref>
ATIS have seven basic processes (sample preparation, image acquisition, image analysis, results reporting, data storage, network communication, and self-system diagnostics) and realization of these functions highly accurate hardware and well-integrated, complex, and expensive software.<ref>{{cite journal|doi=10.1016/j.aca.2005.11.083 |pmid=17723364 |title=Analytica Chimica Acta - Advances in cancer tissue microarray technology: Towards improved understanding and diagnostics |datevolume=564 | volumeissue=5641 |journal=Analytica Chimica Acta |pages=74–81|pmc=2583100|year=2006 |last1=Chen |first1=W. |last2=Foran |first2=D. J. }}</ref>
===Preparation===
Specimen preparation is critical for evaluating the tumor in the automated system. In the first part of the preparation process the biopsied tissue is cut to an appropriate size (typically 4 mm), fixed in buffered [[formalin]], dehydrated in ethanol-[[xylene]], embedded in [[Paraffin wax|paraffin]], [[thin section]]ed typically to 4 um slices, then mounted onto at least two [[barcode]]d slides (a [[scientific control|control]] and a test). Next the paraffin is removed from the tissue, the tissue is rehydrated, then [[staining|stained]]. Any inconsistency in these procedures from case to case may result in uncertainties in the outcome of the analysis. These potential and irreducible inconsistencies in analysis results motivated the development of Automated Tissue Image Systems.
===Acquisition===
|volume=392
|issue=1–3
|pages=220–226.
|journal=Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
|date=February 1997
|doi=10.1016/S0168-9002(97)00297-0
===Analysis===
[[Image analysis]] involves complex computer algorithms which identify and characterize cellular color, shape, and quantity of the tissue sample using image pattern recognition technology based on [[vector quantization]]. Vector representations of objects in the image, as opposed to bitmap representations, have superior zoom-in ability. Once the sample image has been acquired and resident in the computer's random access memory as a large array of 0's and 1's, a programmer knowledgeable in cellular architecture can develop deterministic [[algorithms]] applied to the entire memory space to detect cell patterns from previously defined cellular structures and formations known to be significant.<ref name="han12cell">{{cite journal| author=Han, J.W.| author2=Breckon, T.P.| author3=Randell, D.A.| author4=Landini, G.| title=The Application of Support Vector Machine Classification to Detect Cell Nuclei for Automated Microscopy| journal=Machine Vision and Applications| year=2012| volume=23| pages=15–24| publisher=Springer| doi=10.1007/s00138-010-0275-y| issue=1| s2cid=12446454}}</ref>
The aggregate algorithm outcome is a set of measurements that is far superior to any human sensitivity to intensity or [[luminance]] and color hue, while at the same time improving test consistency from eyeball to eyeball.{{Citation needed|date=August 2010}}
===Reporting===
The systems have the capability of presenting the resulting data in text and graphically, including on high definition monitors, to the system user. [[Computer printers]], as relatively low image resolution devices, are used mostly to present final [[pathology]] reports that could include text and graphics.{{Citation needed|date=August 2010}}
===Storage===
Storage of the acquired data (graphical digital slide files and text data) involves saving system information in a [[data storage device]] system having at least convenient retrieval, and file management capabilities.{{Citation needed|date=August 2010}}
Medical imaging industry standards includes the [[Picture Archiving and Communication Systems]] (PACS), of European origin, which are image and information management solutions in computer networks that allow hospitals and clinics to acquire, distribute and archive medical images and diagnostic reports across the enterprise. Another standard of European origin is the Data and Picture Archiving and Communication System (DPACS). Although medical images can be stored in various formats, a common format has been Digital Imaging and Communications in Medicine ([[DICOM]]).{{Citation needed|date=August 2010}}
== See also ==
* [[Histopathology]]
| 