Method and apparatus for automated analysis of biological specimens

An apparatus and method for analyzing the cell objects of a cell sample for the diagnosis and treatment of actual or suspected cancer is disclosed. An image of the cell sample is first digitized and morphological attributes, including area and DNA mass of the cell objects are automatically measured from the digitized image. The measured attributes are compared to ranges of attribute values which are preestablished to select particular cell objects having value in cancer analysis. After the selection of cell objects, the image is displayed to an operator and indicia of selection is displayed with each selected cell object. The operator then reviews the automatically selected cell objects, with the benefit of the measured cell object attribute values and accepts or changes the automatic selection of cell objects. In a preferred embodiment, each selected cell object is assigned to one of six classes and the indicia of selection consists of indicia of the class into which the associated cell object has been placed. The measured DNA mass of identified cell object fragments in tissue section samples may also be increased to represent the DNA mass of the whole cell object from which the fragment was sectioned.

BACKGROUND OF THE INVENTION 
This invention relates to a system for performing an assay of a biological 
cell sample, and more particularly, for providing an automated method and 
arrangement of measuring attributes of the cells of the sample and 
classifying sampled cells in accordance with the measured attributes. 
The diagnosis/prognosis of a possible cancer typically includes the removal 
of a cell sample, such as a tissue mass, from the patient. Although an 
attending physician may have good intuition regarding the patient's 
diagnosis/prognosis, confirmation of the diagnosis with a histological 
examination of the cell sample removed from the patient is necessary. The 
histological examination entails cell staining procedures which allow the 
morphological features of the cells to be seen relatively easily in a 
light microscope. A pathologist, after having examined the stained cell 
sample, makes a qualitative determination of the state of the tissue and 
reaches a conclusion regarding the prognosis for the patient. While this 
diagnostic method has a long history, it is somewhat lacking in scientific 
rigor since it is heavily reliant on the subjective judgment of the 
pathologist and it is extremely time consuming. 
The optical evaluation of cell samples, and particularly those taken from 
tissue sections, is a difficult procedure. The optical field presented to 
an evaluator is a disordered collection of cell objects, some on top of 
one another and others being only fragments of whole cell objects. The 
optical field shows only boundaries of two-dimensional optical entities 
filled with varying levels of contrast. Some of the overlapped cell 
objects appear to be large and/or dense single cell objects and some of 
the cell object fragments appear to have sufficient size to be whole cell 
objects. Faced with this random cluster of images, the evaluator's 
difficult and time-consuming task is the selection of single whole cell 
objects which can accurately represent the cell sample and the 
classification of those selected objects into categories which 
classification aids in the final diagnosis/prognosis. 
It is well known that the DNA content of cell objects can provide valuable 
information in cancer diagnosis. Systems have been developed which utilize 
the DNA content of cell objects to improve histological examination. In 
U.S. Pat. No. 4,471,043 to Bacus for Method and Apparatus for Image 
Analyses of Biological Specimens, an automated method and a system for 
measuring the DNA of cells are disclosed which employ differential 
staining of the DNA in cell nuclei with a Feulgen stain and image 
processing. After staining, optical fields of the cell sample are 
presented to an evaluator who selects objects for analysis and categorizes 
the selected objects. Certain attributes including the DNA mass of the 
operator selected cell objects are then measured and used to produce 
reports such as DNA histograms. 
The arrangement and method of Bacus U.S. Pat. No. 4,471,043 have been well 
received both for the reports generated and for the improvements in the 
use of operator time. The operator, however, must still select relevant 
cell objects from the optical field presented and classify the selected 
cell objects into classes before machine measurement of attributes occurs. 
Such selection and classification requires the thoughtful review of each 
object in the random cluster of images of an observed field. Further, the 
only input information available for such review is the varying contrast 
levels presented by the visual image. When the operator must evaluate cell 
samples for a long period of time, as is the case in some pathology 
laboratories, concentration by the operator and accuracy of the decisions 
made, may be affected. 
A need exists for an automated method and arrangement for use with a DNA 
analysis apparatus, which selects whole, single cell objects and 
classifies each selected cell object as being in a particular one of a 
plurality of diagnostic aiding categories as well as in particular regions 
of the DNA distribution. The automatic selection and classification of 
cell objects speeds analysis and reduces the tedium of the operator. Also, 
pre-selection and classification by the apparatus permits the operator to 
concentrate his or her efforts on the difficult and subtle analysis of the 
preselected cell objects which are likely to be representative of the 
sample. 
The evaluation of cell objects and their accurate reporting is at its most 
difficult when a sample is taken from a tissue section. The act of slicing 
the tissue section also slices and distorts some of the cell objects which 
are to be observed and analyzed. The viewed field of a tissue section 
sample contains many small cell object fragments which have almost no 
analysis value, but must be evaluated by the observer. Other fragments are 
substantial parts of cell objects, which due to size or optical density, 
will be selected and reported as whole cell objects. When reports such as 
DNA histograms are prepared, a selected fragment consisting of 75% of a 
whole cell object will be counted as a whole cell object having 
approximately 75% of the DNA contained by the original (pre-sliced) cell 
object. Thus, the histogram will include a cell object having smaller DNA 
mass value than should have been reported. Greater report accuracy can be 
achieved when the cell object attributes such as DNA mass of fractional 
cell objects can be corrected to represent whole cell objects before those 
attributes are used in preparing the final reports. 
A need exists for evaluation methods and apparatus which identify cell 
object fragments likely to possess analysis value and properly correct 
their measured attributes to reflect what those attributes would have 
been, had the identified cell object not been fragmented. 
SUMMARY OF THE INVENTION 
The present invention provides a method and apparatus for automatically 
measuring on a per cell object basis the DNA mass and other morphological 
characteristics of the cell objects in a cell sample and automatically 
classifying each analyzed cell object based on the measured 
characteristics. The DNA mass and the other morphological attributes 
measured are compared with predetermined standard ranges to select single, 
whole cell objects and determine the class of the cell objects being 
analyzed. Both the DNA mass and cell class are displayed in an associated 
manner, and both are used in the preparation of final reports. The present 
invention also provides a method for increasing the measured DNA mass 
value of identified cell object fragments from tissue section samples to 
more accurately represent the DNA mass of the whole cell object from which 
the fragment was sectioned. 
In accordance with the present invention, a method of analyzing the cell 
objects of a cell sample includes automatically measuring predetermined 
attributes, including the DNA mass, of the cell objects and automatically 
classifying selected ones of the cell objects into one of a plurality of 
mutually exclusive categories. An image of the cell objects is then 
displayed in association with their assigned class to an operator for 
human review. The operator is enabled to change the classification of any 
displayed cell object and to "deselect" automatically selected cell 
objects. As an aid to the operator's review of the cell objects, the 
automatically measured attributes are displayed to the operator during 
review. In an embodiment, the cell objects of interest are cell nuclei and 
the automatically measured attributes of the cell objects, which remain 
selected or are newly selected by the operator during the review, are used 
to generate reports such as the DNA histograms. 
The automatic attribute measurement and automatic selection and 
classification of cell objects rapidly produces a first accurate screening 
of the cell objects, removing much operator time and tedium from the 
analysis. The apparatus, in essence, automatically selects cell objects 
which are likely to have value in the assessment of possible cancers. The 
display of cell objects to the operator, along with their automatically 
assigned class, permits the operator to exercise seasoned judgement in the 
selection and classification operation and to change the automatically 
determined selection and classification. At the completion of the 
operator's review, the cell objects remaining selected and in classes are 
likely to be more accurate than those produced by the operator alone, 
since the determinations are made on the basis of quantitative apparatus 
selection and qualitative operator review. 
The automatic selection of cell objects from a cell sample and their 
classification is based on a set of filters, each comprising a group of 
cell object attribute value ranges. In a preferred embodiment, value 
ranges are established for the following cell object attributes: area, 
perimeter, DNA mass and shape. The value ranges for the filters are 
dependent on the type of analysis to be performed and are established by 
the operator prior to the actual analysis of cells. In an exemplary 
embodiment, the attribute range values for prostate tissue sections are 
disclosed. 
Up to six different filters are established for a given analysis, each 
filter being used to select one class of cell objects having a 
preestablished set of value ranges. The definition of what each class 
represents is left to the individual establishing the attribute value 
ranges. The definitions of the six ranges in an exemplary embodiment for 
analyzing prostate tissue sections are diploid, large diploid, irregular 
diploid, S-phase, tetraploid and massive DNA. 
After the filter value ranges are established, an image of a field of the 
cell sample is digitized and the attributes of the cell objects of the 
digitized image are measured by the apparatus. The measured attributes are 
then compared with the stored filter-defining attribute value ranges to 
determine if a cell object observed in the image meets the definition of a 
class as established by the filter for that class. When a cell object is 
within the value ranges of a filter, the cell object is said to be 
selected and it is assigned to the class into which it falls. 
Alternatively, when a cell does not fall within any class it is said to be 
not selected. 
Upon the completion of automatic attribute measurement and class 
assignment, a representation of the cell sample image is displayed to the 
operator. The cell objects which were assigned a class are displayed in 
association with indicia of that assigned class. In a preferred 
embodiment, each class of cell object is assigned a unique color and each 
cell object in a class is displayed with a perimeter having the unique 
color of its class. Surrounding classified cell objects with the color of 
the class assigned thereto, notifies the operator of the class of the cell 
object but does not obscure the cell object from the operator's view. 
In a review phase of the cell analysis, the operator surveys the 
classified, displayed cell objects to determine if any assigned class 
should be changed or if a classified cell object should have its class 
removed. A computer mouse is used to move a cursor to a displayed cell 
object of interest and the cell object can be chosen for review by 
pressing a mouse key. Upon choosing a cell object for review, the measured 
attributes of that cell object are displayed so that the operator can see 
the qualitative attributes of that cell object. Based on the cell object 
display and the displayed attributes, the operator can change the class of 
the chosen cell object and thereby change its enhanced perimeter. When the 
cell objects selected and their assigned classes agree with the operator's 
opinion, the field can be approved. The measured attributes of the cell 
objects which remain in a class after the review phase are used to 
generate reports such as DNA mass histograms. 
Cell object samples taken from tissue sections may include cell object 
fragments which are selected and reported as whole cell objects while 
their DNA mass is less than the DNA mass of the cell object from which the 
fragment was sectioned. With the present invention, the measured DNA mass 
of such fragments can be corrected (increased) to reflect the DNA mass of 
their source whole cell object. After the automatic measurement of cell 
object attributes, cell object fragments which are likely to have analysis 
value are identified from their measured attributes and the cell object 
attributes are corrected to reflect the attributes of the whole cell 
objects from which the fragment was sectioned. 
In a preferred embodiment, the valuable cell object fragments are 
identified by comparing the measured cell object area with a threshold 
value determined from the known thickness of a tissue section from which 
the sample was taken. If the measured cell object area is larger than the 
threshold, the cell object is identified for correction since it is too 
large to be entirely included within the tissue section. A correction 
value is then determined from the measured area and the tissue section 
thickness and used to increase the measured DNA mass of the identified 
cell object fragment. 
In an embodiment of the invention, the correction value C is determined 
from the equation: 
##EQU1## 
where T is the tissue section thickness and R equals the square root of 
the measured area divided by .pi.. The formula yields a correction value 
between 0 and 1 for identified cell objects, which value is divided into 
the measured DNA mass of the cell object to increase the DNA mass to a 
corrected value representing the whole cell object. The correction values 
can be calculated and the division performed as each identified cell 
object is classified, or it can be applied to a stored list of measured 
cell objects selected from the filter process discussed above. Also, a 
correction value look up table can also be created before analysis begins. 
Such a correction value table stores predetermined calculation results 
from various combinations of tissue section thickness and measured cell 
object area. Similarly, the correction value table can be populated with 
correction values empirically determined from studies of tissue section 
cell object samples. 
The DNA mass correction function can be performed before cell objects are 
selected and classified for reports, or after such selection and 
classification.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
As shown in the drawings and described herein, the invention is embodied in 
a method and apparatus for automatically analyzing "cell objects" which 
term is used to be generic to cells such as cells taken from tumors or the 
like, and which are checked for their DNA content and also to be generic 
to non-biological objects such as plastic or glass spheres. In the 
disclosed embodiment, the cell objects of interest are cell nuclei. By way 
of example, the present invention is useful for study not only of ploidy 
analysis but also can be used to analyze Pap smear cells, monoclonol 
antibodies and other infectious diseases which can be diagnosed by DNA 
probes. 
The Preferred Embodiment described herein is used to analyze tissue section 
biological specimens which are examined on a microscope slide. The 
principles taught however relate also to other sample collection methods 
such as cell aspiration and to other means for examining cells such as a 
flow cytometer. 
The present invention speeds up cell analysis by improving the level of 
automation and providing new and valuable information to the medical 
expert responsible for diagnosis/prognosis. To illustrate the importance 
of accurate cell object selection for analysis by the present apparatus, 
reference is directed to FIGS. 3 through 6. FIG. 3 shows a normal DNA mass 
histogram having cell number (count) versus mass distribution for healthy, 
non-dividing cells. The number of cells is provided on the ordinate axis 
and their DNA nuclear mass on the abscissa. The cell population shown in 
FIG. 3 is not dividing and the DNA content peaks around a normal peak 
G.sub.0 /G.sub.1 which is the diploid DNA amount of 7.18 picograms per 
cell. This relative mass of DNA is labelled as 1 to normalize the abscissa 
of the histogram. FIG. 4 shows a normal cell population which is dividing 
as is shown by the significant G.sub.0 /G.sub.1 peak at 1 and a second 
peak G.sub.2 at 2 (14.36 picograms per cell). The peak at 2 is normal, 
because some of the cells are in division and have double the normal 
diploid amount of DNA. The saddle S between the two peaks represents cells 
which are in various stages of DNA replication. 
Comparing the histogram of FIG. 5 with those of FIGS. 3 and 4, it is seen 
that this cell population is skewed from normal having a first peak around 
1.5 and the second peak around 3. This histogram may show a malignancy 
because of the abnormally high DNA content of many of the cells. This high 
DNA content is likely indicative of the increased chromosome content of 
malignant cells. Similarly, in FIG. 6 it is shown that the first peak 
occurs at 1 indicating the normal diploid amount of DNA, but has a 
relatively large trailing saddle from 1 to 2.8. A normal second peak is 
not shown in FIG. 6. The shape of the FIG. 6 histogram is likely due to 
abnormal DNA amounts in cells and clones of cells indicative of 
malignancy. The analysis of FIGS. 5 and 6 is a difficult task because the 
particular classes of cells which make up each mass range of the histogram 
can be important to analysis, but can only be surmised from the data 
provided by the histogram. The disclosed embodiment automatically 
determines a cell class from measured DNA content and other morphological 
features of cell objects and provides this cell category information to 
the expert as an aid to his or her analysis. 
The DNA histograms of the type shown in FIGS. 3 through 6 are extremely 
valuable in the histological evaluation of both suspected and actual 
cancers. Given their value, it is important that the data from which the 
histograms are generated is accurate and can be produced at a reasonable 
cost so that these techniques are widely available. 
FIG. 7 is a representation of a portion of a disordered collection of cell 
objects as seen in an optical image of a cell sample. The representation 
of FIG. 7 includes five optical entities numbered 110 through 114. An 
optical entity, in this description, is a viewable item having a common 
boundary filled with levels of contrast so that it can be distinguished 
from the image background. An optical entity may comprise a single cell 
object fragment, a single whole cell object or overlapped combinations of 
whole and fragmentary cell objects. The cell object fragments are 
particularly prevalent in cell samples taken from sectioned tissue where 
the slicing necessary to prepare the section cuts many cell objects into 
fragments. The optical entities of FIG. 7 are made up of whole cell nuclei 
and fragments of cell nuclei. When these optical entities are to be 
evaluated manually, an evaluator selects which of the optical entities 
should be included in the final tabulation such as the histograms of FIGS. 
3 through 6. 
Entity 110 is relatively round and appears to have sufficient density 
(contrast) to be a whole single cell object, and will probably be selected 
for analysis as a diploid cell by the evaluator. Entity 111 may be 
interpreted as a single elongated cell object of sufficient density for 
evaluation, however, it may actually be two cell objects 116 and 117, 
lying partially one on top of the other, to give the appearance of a 
single cell object Should the evaluator select entity 111 as a single cell 
object, when in fact it is two cell objects, the data will show a single 
cell object (a count of one on the histogram) which has a DNA mass of 
approximately twice the diploid amount. Thus, the selection of optical 
entity 111 may result in incorrect data. Similarly, entity 113 may be 
interpreted as two overlapping cell objects and not counted by an 
evaluator. However, entity 113 may be a deformed tetraploid cell object in 
which case, the selection of optical entity 113 would be valuable to a 
final report. It can be seen from FIG. 7, that the selection of cell 
objects which are likely to provide accurate analysis information is a 
difficult task when only the optical image is available. The present 
invention automates cell sample evaluation including cell object selection 
and classification by the apparatus shown in FIGS. 1 and 2, functioning in 
accordance with new operational methods. 
In the implementation shown in FIGS. 1 and 2, the system is a computerized 
image analysis apparatus designed to measure a number of cell object 
features and parameters from their image on a typical glass slide. The 
apparatus includes a sophisticated digital image processing system which 
performs quantitative analysis on individual cells for nuclear DNA content 
as well as measurement of other nuclear features. 
FIG. 1 shows an apparatus embodying the present invention which is 
generally identified by a numeral 10. Apparatus 10 comprises an optical 
microscope 12 which may be any conventional type, but in this embodiment 
is a Riechart Diastar. An optical camera module 14 is mounted on the 
microscope 12 to enhance optically a magnified image of a cell sample 
viewed with microscope 12. The optical camera module 14 includes at least 
one television camera which generates a standard NTSC compatible signal 
representative of the field of view of microscope 12. An image processing 
system 28 (FIG. 2) is connected to the camera module 14 to receive the 
NTSC signal and to store a cell object pixel array therein. The image 
processor 28 is connected to a computer 32 which in the present 
embodiment, is an INTEL 301, Model 386 computer, for the processing of the 
pixel array. 
Computer 32 which is shown in greater detail in FIG. 2 includes a system 
bus 34 connected to the image processor 28. An 80386 microprocessor 36 is 
connected to the system bus 34. A random access memory 38 and a read-only 
memory 42 are also connected to system bus 34 for the storage and 
information. A disk controller 40 is connected to the system bus 34 and by 
a local bus 44 to a Winchester disk drive 46 and to a floppy disk drive 48 
for secondary information storage. A video conversion board 50, in this 
embodiment an EGA board having 256 K bytes of memory, is connected to the 
system bus 34 to control an instruction monitor 52 connected to the EGA 
board 50. A keyboard processor 54 is connected to system bus 34 to 
interpret signals from a keyboard 56. An interactive computer mouse 20 is 
also connected to bus 34 via a mouse interface 23. A printer 58 is 
connected to the system bus 34 for generating paper copies of information 
generated by computer 32. 
An X-Y image field board 60 is connected to the system bus 34 and to a 
slide holder of the microscope 12 to sense the relative position of slide 
62 with respect to a microscope objective 64. Included is a Y position 
sensor 66 and an X position sensor 68. The Y position sensor 66 is 
connected via a communication path 70 to the X-Y board 60 and the X 
position sensor 68 is connected via a communication path 72 to the X-Y 
board 60. The microscope 12 also includes an eye piece 76, in optical 
alignment with the objective 64 for the magnification of light forming the 
image of a cell sample on slide 62. 
When using the apparatus 10, a pathologist first collects a cell sample 
which may be in the form of a tissue section made from frozen or 
paraffinized prostate tissue. Such a cell sample will include both whole 
cell nuclei and cell nuclei fragments, both of which are referred to as 
cell objects herein. Alternatively, the cell sample may be a cell 
preparation of the type taken by aspirating the contents of a cyst or 
tumor. The cells of the cell sample are placed on slide 62 and fixed 
thereon. The fixed cell sample is then prepared by, for example, the 
Feulgen staining technique to enhance cell object features. 
The microscope slide 62 is then placed on the carrying stage of microscope 
12 and the objective 64 is focused thereon. A portion of the light from 
the objective 64 travels through a beam splitting mirror 80 to eye piece 
12 where it may be viewed by an observer. The beam splitting mirror 80 
conveys the remainder of the light from objective 64 to camera module 14, 
which generates an NTSC signal representing the image and applies that 
signal to image processor 28. Image processor 28 digitizes the image 
received from camera unit 14 and stores the digitized image in a frame 
buffer of the image processor. The contents of the frame buffer are 
presented to an image monitor 30, which displays a field of the cell 
sample. The digitized image field is also presented to computer 32 over 
system bus 34 for analysis thereof. 
When the apparatus is in operation, the operator has a number of options or 
functions which can be chosen to acquire and process data from a cell 
sample. In general, the program setting forth the functions is menu driven 
and provides on instruction monitor 52 a main menu of options as shown in 
FIG. 8. The main menu 120 consists of five main screen functions including 
a label function 122, a calibrate function 124, a help function 126, an 
exit function 128 and an analyze function 130. A function is selected from 
the menu of FIG. 8, as well as other menus presented to the operator, by 
operator interaction with mouse 20. The menus are presented on instruction 
monitor 52 to the operator who moves a cursor thereon by means of mouse 20 
and selects a desired function by pressing a mouse button e.g., 21 when 
the cursor is on the desired function. Mouse interactive menu selection is 
well known in the art. 
The label function 122 allows a user to enter information regarding the 
patient identification, accession number, and a DNA conversion number. The 
DNA conversion number is the expected DNA mass for a diploid cell. 
Initially, the DNA conversion number is set by default to a standard 7.18 
picograms for normal human cells. However, the apparatus may be used to 
measure non-human cells and the index may be changed to the number 
desired. The DNA index number must be set to a value greater than or equal 
to 1 and less than or equal to 99.99. 
Selection of the calibrate function 124 permits the proper adjustment of 
the apparatus to assure accurate measurements. In the calibrate function 
124 light levels are compared to known standard light levels for 
appropriate adjustment and the DNA mass of a number of standard cells is 
determined so that the DNA mass of the cell objects in the cell sample can 
be accurately determined. The calibration of apparatus of the type shown 
in FIG. 1 is described in detail in U.S. Pat. No. 4,741,043 to Bacus. 
The help function 126 of FIG. 8 provides explanation to the operator to aid 
in the use of the apparatus 10. The exit function 128 permits the system 
to leave the present main menu function. 
An analysis analyze function 130 is performed to specifically analyze the 
cell objects of a sampled field of cell objects. The menu 142 for the 
analyze function is shown in greater detail in FIG. 9. It is in the 
analysis function of FIG. 9 that actual cell object attributes such as DNA 
mass are measured and recorded for reporting purposes. The analyze 
function 130 includes a number of sub-functions which are employed to 
improve the accuracy of cell object attribute measurement. One 
sub-function is the check light function 148 which calculates the light 
level of the current field image to assure accuracy of adjustments. A 
boundary function 156 allows the operator to set the threshold which must 
be achieved between the image background and the contrast of an optical 
entity before that entity is recognized. Scale function 154 permits the 
adjustment of the horizontal scale of DNA histograms shown on the monitor 
instruction 52. The set thickness function 146 is used to provide certain 
DNA mass corrections and is discussed below. The analyze function also 
includes a help function 158 to provide assistance to the operator and a 
main function 162 which permits the return to the main menu from the 
analysis menu 142. 
The classify function 144 and set filters function 150 are the two most 
closely related functions to the actual evaluation and measurement of cell 
object attributes. The set filters function allows the operator to 
establish ranges of predetermined measurable parameters which are used to 
interpret the attributes of cell objects measured in the classify 
function. 
The ranges are set by the operator and define which cell objects will be 
automatically selected for reporting and what classes of cell objects are 
to be automatically assigned to the selected cell objects The filter 
ranges are, as discussed below, compared to automatically measured cell 
object attributes and cell objects will be selected and classified or not, 
depending on their similarity to the established ranges. The setting of 
ranges, the automatic measurement of cell objects and the comparison of 
measurements and filter ranges, replaces the tedious cell object selection 
and classification by human operators. 
When the set filters function is requested, a filter menu 171 of FIG. 14 is 
displayed on the analysis screen of instruction monitor 52. The filter 
menu 171 includes an options field 170 and a filter parameter field 172. 
Options field 170 sets forth particular functions which can be performed 
in the set filters function and the parameters field 172 identifies the 
value ranges of four cell object attributes which are used to 
automatically place a given cell object into one of six classes identified 
as class 1 through class 6. When the filter menu is initially presented to 
the operator on monitor instruction 52, the four attribute value ranges 
under each class heading are all set to zero. 
The options listed in the options field 170 are primarily used to establish 
ranges of values in the parameter field 172 and to define the use of those 
parameters. The "Load" option 175 permits the operator to specify the name 
of a pre-stored file of filter parameters which are read from memory and 
used to populate the filter parameter field 172. The "New" option 176 
permits the operator to directly enter ranges of values into parameter 
option 172 for each cell object attribute in each class 1 through 6. The 
"Save" field 177 permits the saving of the values written into parameter 
field 172. The option "Save As" is similar to the option "Save" 177, 
except that the saved set of parameters can be given a particular name to 
aid in later recall. The "Delete" option 179 is used to delete all of the 
parameters entered in parameter field 172 and the "Filter Off" option 180 
enables the use of the classify function 144 without any preassigned 
filter parameters. As in the previous screens, the "Help" function 181 
provides user information and the "Exit" option 182 allows the user to 
return to the analysis menu 142. 
The parameter field 172 includes filter definitions for each of six classes 
of cell objects. The filter for each cell object class comprises a range 
of values assigned to each of four cell object attributes listed below 
that class designation. The four attributes for each class are the area of 
optical entity in square micrometers, the shape of the optical entity, its 
DNA mass in picograms and the optical density of that optical entity. The 
shape factor in the value ranges is determined by dividing the square of 
the perimeter of the optical entity by the area of that entity Thus, a 
round shape has shape value of 4.pi. and the value becomes larger the 
"less round" the measured object is. 
The filter parameters placed in the class definition field 172 depend on 
the particular location from which the cell sample was taken e.g., breast 
and the particular type of cell sample e.g., tissue section The ranges of 
values shown in FIG. 14 have been found useful in the evaluation of 
prostate tissue section cell samples. With the values shown, class 1 
identifies small diploid cell objects which are substantially round; class 
2 identifies larger diploid cell objects which are also substantially 
round; class 3 identifies S phase cell objects which are of regular shape; 
class 4 identifies small diploid cell objects of a more irregular shape 
than class 1; class 5 identifies tetraploid cell objects; and class 6 
identifies cell objects having large amounts of DNA. 
After the filter values are established in function 150, the operator can 
move to the classify function 144 in which cell objects are automatically 
selected by the apparatus 10 in accordance with the established filter 
values. In the case where a cell object does not meet the filter ranges 
for any of the six classes (FIG. 14), the cell object is not selected for 
classification. 
The flow chart of the classify function 144 is shown in FIG. 10. In the 
classify function, the digital image of a field of optical entities on 
slide 62 is analyzed by microprocessor 36 (FIG. 2). The digitized image is 
scanned (block 191) until an optical entity is found by the difference in 
contrast values between the entity and the image background. 
Microprocessor 36 then proceeds to the measure attribute step 193 where it 
identifies the perimeter of the found optical entity and counts both the 
number of pixels on the perimeter and the number of pixels within that 
perimeter to determine the perimeter and area of the entity found. Also, 
microprocessor 36 computes the DNA mass of the found optical entity from 
the previous calibration data and the density of the cell object and forms 
a sum of the optical density of the entity. After the attributes have been 
measured, an optical entity table (FIG. 11) is established for the optical 
entity and the measured attributes are stored in the entity table in step 
195. The optical entity table for an entity includes its area, shape, 
class, DNA mass, optical density, perimeter length and location 
information. The location information of an entity consists of the X and Y 
coordinates of the field containing the entity, as determined from the X 
position sensor 68, the Y position sensor 66 and the X-Y board 60 and the 
horizontal and vertical maxima and minima of the entity perimeter as 
determined from the digital representation of the field. When the optical 
entity table is initially established for an entity, the class designation 
is set to 0 to indicate an unselected entity. The class designation for an 
entity which is assigned a class 1 through 6 is written into the entity 
table of that entity in later step 199. 
After the optical entity attributes are accumulated and stored, they are 
compared (step 197) with the value ranges established in the filter 
setting function 150 (FIG. 9) in order to classify the entity. The optical 
entity attributes of an entity are compared to the six sets of filter 
ranges 1 through 6 in sequence. When the optical entity is found to be 
within a class, the entity is assigned to the found class and no further 
comparison with the ranges of other classes is undertaken. The following 
is an example of comparison for an optical entity having an area 80, shape 
14, DNA mass 7.18 and density 0.4. The exemplary entity attributes will be 
first compared to ranges of class 1, where no match will be found because 
the entity area is too large. The entity attributes are next compared with 
the ranges of class 2 where a match is found. The entity is then assigned 
to class 2 and further comparisons with class ranges for this entity do 
not occur, even though this entity might fit into more than one class. 
When the measured values of an entity do not fall within all of the ranges 
of values of any class, the entity is determined in step 198 to not be a 
useful diagnostic entity and the control program returns the find 
unmeasured entity step 191 to identify a new optical entity. 
Alternatively, when the measured attributes fall within all of the ranges 
of values of one of the classes 1 through 6, the identified class is 
stored (step 199) in the optical entity table (FIG. 11) of the found 
entity. After step 199, the flow proceeds to a block 321 to determine if 
DNA correction is needed. For the present example, the DNA correction, 
which is discussed below, is not needed and the program returns to block 
191 to search for another optical entity. 
When all of the optical entities in a field have been measured, no 
unmeasured object will be found in step 191 and flow proceeds to a review 
function 201. The review function 201 begins with the display (FIG. 12) on 
image monitor 30 of the optical field presently being reviewed. The 
displayed field is enhanced by drawing a line around each optical entity 
which was assigned one of the classes 1 through 6 (step 197, FIG. 10). The 
line around the perimeter is called perimeter enhancement and indicates 
that the included optical entity has been selected and classified. All 
non-selected optical entities in the field are displayed without perimeter 
enhancements. 
The perimeter enhancement for a selected optical entity consists of a line 
around the selected optical entity, having a color which is uniquely 
associated with the class assigned to that optical entity. The association 
of classes 1 through 6 and colors is represented in FIG. 14 by a 
rectangle, e.g., 183 next to the designation of each class, e.g., class 1. 
In FIG. 14, rectangles 183 through 188 are shown in association with 
classes 1 through 6 respectively. The color of the rectangles 183 through 
188 is described in Table 1. 
TABLE 1 
______________________________________ 
Rectangle Class Color 
______________________________________ 
183 1 (small diploid) 
Light Blue 
184 2 (large diploid) 
Red 
185 3 (S phase) Green 
186 4 (irregular diploid) 
Dark Blue 
187 5 (tetraploid) Violet 
188 6 (massive DNA) Yellow 
______________________________________ 
The encirclement of an optical entity by a colored line provides the 
operator with a direct indication of the class assigned to that optical 
entity, but does not obscure the optical entity from view. 
The review image represented in FIG. 12 includes five optical entities 250 
through 254 of which entities 250 and 254 do not have perimeter 
enhancements, while entities 251, 252 and 253 are encircled by perimeter 
enhancements 261, 262 and 263 respectively. For purposes of illustration, 
it is assumed that entity 250 is a cell object fragment having DNA mass of 
less than 6.47 picograms and that entity 254 has a DNA mass between 7.97 
and 12.91 picograms, but is too irregular (shape greater than 16) to be an 
S phase cell. Thus, the entities 250 and 254 were not selected to be in a 
class and are not reproduced with enhanced perimeters. Entity 251 is 
produced on display 30 and represented in FIG. 12 with a light blue 
perimeter 261, (-.-.-.-) indicating a diploid cell object, entity 252 is 
produced with a green perimeter 262, (....) indicating an S phase cell 
object and entity 253 is produced with a violet perimeter 263, (----) 
indicating a tetraploid cell object. The cell objects and their enhanced 
perimeters are observed by the operator during the review function. 
The review function is an operator interactive operation, the menu for 
which is represented in FIG. 13. In the review function 201, the operator 
can select a help function 204 or an exit function 206 which are 
substantially similar to the previously described help and exit functions. 
Should the operator select the exit function 206, classifications assigned 
to the optical entities displayed (FIG. 12) are retained and the measured 
attributes of those selected entities will be used to produce final 
reports such as the DNA histogram. If the operator feels that further 
review of the field is desirable, he or she selects the "choose entity" 
function 208 which produces a mouse cursor 210 on the field viewing screen 
30 represented in FIG. 12. By manipulation of mouse 20, the mouse cursor 
210 is placed on an optical entity e.g., 253 (either previously classified 
or previously not classified) in question and that entity is chosen for 
review by depressing a key 21 on the mouse 20. When an entity is chosen 
its entity table (FIG. 11) is identified for use by comparing the X, Y 
position of the cursor 210 with the X and Y location information of 
optical entities as previously stored in the entity tables. After choosing 
an entity for review, the operator has three additional functions which 
can be selected. The display attributes function 214 produces on the 
review screen of FIG. 12, an attributes field 215 which displays the 
previously measured and stored attributes of the optical entity chosen for 
review. The attributes displayed in field 215 provide additional 
information to the operator on which decisions can be made. 
Based on the viewed optical entity and the displayed attributes in field 
215, the operator may wish to change the class to which the chosen entity 
was previously assigned. Change class function 212 is used to provide this 
change. The selection of the change class function 212 displays a 
numerical listing 217 (FIG. 12) of the numbers 1 through 0. The operator 
can place the mouse cursor 210 on any of the numbers 1 through 6 or 0 and 
press button 21 to enact a change of classification. Selecting one of the 
numbers 1 through 6 changes the classification of the chosen entity to the 
new class number. If the class number 0 is identified from the list 217, a 
0 is written into the class location of the entity table. Upon a change of 
class the new class number (or zero) is stored in the associated entity 
table (FIG. 11) and, the colored perimeter of the selected entity is 
changed to a color representing the newly assigned class or is changed to 
a dark green indicating a deselected entity. 
While in the choose entity function 208, the operator may observe two 
overlapped cell objects which appear to have been considered during 
automatic classification as a single optical entity. The operator can move 
the cursor to select such an entity. The cut entity function 216 can then 
be selected in which the operator, using known techniques, separates the 
selected optical entity into two parts for analysis purposes. After the 
cut entity function 216 the control program returns to the flow chart of 
FIG. 10 to reevaluate the cut entity as two entities in accordance with 
the operator's direction. At the conclusion of the reevaluation of the two 
new entities control returns to the review menus 201 in the choose 
function 208. The newly cut entity is then displayed, if appropriate, with 
enhanced perimeters around either portion of the entity. 
At the conclusion of the choose entity function 208, the operator returns 
to the exit function 206 which transfers control back to the analysis menu 
142 (FIG. 9). In the analysis menu, the operator can select the report 
function 160 to prepare reports such as a DNA histogram. The data for 
reports is read from the entity tables of the optical entities of the cell 
sample. Since each entity table includes a class designation identifying 
the class of the associated entity, the unselected entities (class=0) can 
readily be excluded from reports. 
The previously described operations both simplify and accelerate the cell 
analysis operation by assisting an operator in the analysis process and 
automatically performing many of the routine portions of the analysis. 
Although difficult to quantify, the operator assistance provided by the 
present apparatus is believed also to improve the accuracy of final 
analysis and reporting. An additional capability, described below, 
directly improves the accuracy of final reports particularly DNA histogram 
reports when they result from an analysis of cells prepared from tissue 
sections. 
A tissue section sample is made by "hardening" and "embedding" a tissue 
sample removed from a patient, perhaps by immersing the tissue sample in a 
fixative like formalin, and then by allowing it to be permeated by 
paraffin. To achieve a section, thin e.g., 5 micron slices are cut from 
the tissue mass. The act of cutting thin slices, which in many cases are 
thinner than the major dimensions of the cell objects to be observed, 
creates many cell object fragments. 
FIG. 15 is an edge view of a tissue section 300 of thickness T showing a 
plurality of cell objects 301 through 305. Tissue section 300 has a top 
surface 310 formed by one slicing operation and a bottom surface 311 
formed by another slicing operation. FIG. 16 is a top view of the same 
tissue section which represents the view of FIG. 15 presented to analysis 
apparatus. In FIG. 15, the solid line cell objects and cell object 
fragments are those remaining for analysis and the dotted lines above and 
below surfaces 310 and 311 represent the portions of cell objects which 
have been sliced away by tissue sectioning. Cell objects 301 and 302 
represent small fragments of relatively small cell objects. Cell objects 
303 and 304 represent whole cell objects and cell object 305 represents a 
large cell object with relatively small top and bottom portions sliced 
away. 
By presumption, fragments 301 and 302 will not be selected during analysis, 
since they are so small and lack sufficient density and DNA mass to match 
any of the filters of FIG. 14. Cell objects 303 and 304 are presumably 
whole diploid cell objects and will be selected by the automated process. 
Cell object fragment 305, which is assumed to represent a tetraploid cell, 
will be selected by the filters but its measured DNA mass value will be 
less than its unfragmented value. That is, the DNA content of the sliced 
away dotted line portions will not be included in its DNA mass. Thus, 
reporting fragment 305 as a whole cell object on the DNA mass histogram, 
will cause a point to appear with less mass than it should have. 
For purposes of the following example, it is assumed that cell objects 303 
and 304 have measured areas of 19 square microns and are selected as class 
1 cell objects and that cell object 305 has a measured area of 50 square 
microns, a DNA mass of 11.7 picograms and will be selected as a class 3 
cell object. The present embodiment includes methods for correcting the 
DNA mass values of cell object fragments, such as fragment 305 to more 
accurately represent their unfragmented DNA mass. Both the need for DNA 
mass correction, and the amount of correction is dependent on the 
thickness of the tissue section which was used to provide the cell sample 
being analyzed. The tissue section thickness is established in the 
apparatus by performing the set thickness function 146 of the analysis 
menu of FIG. 9. For the purposes of the present example, the tissue 
section 300 has a thickness T of 5 microns, so that the value 5 microns is 
entered in the set tissue thickness function 146. 
A flow diagram of the DNA mass correction operation is shown in FIG. 17 
which is an adjunct to the classify flow diagram of FIG. 10. The classify 
function is performed as shown in FIG. 10 and when a cell object such as 
the cell object fragment 301 is not selected for classification, the flow 
proceeds from block 198 to block 191, as previously discussed. When a cell 
object is selected for classification, the assigned class is written into 
the entity table (FIG. 11) of that cell object and the flow proceeds to 
block 321 to determine if correction is to be invoked. When correction is 
to be invoked, flow proceeds to the correction function of FIG. 17. The 
correction function begins in block 320 (FIG. 17) where the measured area 
of the cell object is compared to a threshold value determined from the 
tissue section thickness. The threshold value is the area of a circle 
having a diameter equal to the tissue section thickness. For the 5 micron 
tissue section of the present example, threshold of block 320 is 
approximately 20 square microns. Block 320 is performed to identify large 
cell fragments such as cell object fragment 305, which need correction and 
to not identify for correction smaller whole cell objects such as 303, 
which do not need correction. 
When whole cell object 303 (area=19 square microns) is being classified, 
step 320 determines that the threshold of 20 square microns is not 
exceeded and the flow proceeds to block 191 without correcting the DNA 
mass value of whole cell object 303. When cell object fragment 305 is 
classified, it will be placed in class 3 (see example assumptions) and 
block 320 will determine that the measured area (50 square microns) is 
larger than the 20 square micron threshold. A measured area greater than 
the threshold indicates that the entire cell object is unlikely to exist 
in the 5 micron thickness, and that DNA mass correction is needed. After a 
determination that the measured area of a cell object exceeds the 
threshold, a block 322 is performed to determine a correction value for 
the cell object. The correction value is determined from the measured area 
of the cell object and the thickness of the tissue section sample. 
The correction value C is calculated from equation 1: 
##EQU2## 
where T is the tissue section thickness and R equals the square root of the 
measured area divided by .pi.. Given the 50 square micron measured area of 
cell object 305, R is approximately equal to 3.99 microns yielding a 
correction value C of 0.817. After the correction value is determined in 
block 322, the measured cell object mass is divided thereby in block 324 
to increase the DNA mass to a corrected value. In the present embodiment, 
the measured mass of cell object 305 (11.7 picograms) is increased to 
14.32 picograms in block 324. The corrected mass value, which more 
accurately represents the mass of cell object 305 before sectioning, is 
then recorded for use in the preparation of reports such as DNA histograms 
From block 324, the flow proceeds to block 191 (FIG. 10). 
In the previous example of the correction function, a correction value was 
calculated for each cell object fragment identified to have an area 
greater than a predetermined threshold. The equation need not be performed 
for each identified cell object. Instead, the equation can be performed 
for many hypothetical combinations of tissue section thickness and 
measured cell object area and the results (the value C) stored in a look 
up table. This table can then be accessed directly from the tissue section 
thickness and cell object area to speed processing. Additionally, other 
methods for populating the correction value look up table could be used. 
For example, empirical data correlating measured cell object fragment area 
and the actual DNA mass of whole cell objects may be obtained from tests 
and used to populate the correction value look up table. 
In the preceding example, cell objects were selected and assigned to 
classes before correction of DNA mass was performed. The DNA mass 
correction of FIG. 17 could have been performed before selection. In such 
a case, the function performed by FIG. 17 could be entered after block 195 
(FIG. 10) with a return from the correction function to block 197. In this 
way, classes would be assigned to identified cell objects on the basis of 
corrected cell object mass values. Some cell evaluation systems measure 
DNA in relative terms and assign cell objects a relative DNA index value. 
The present invention can be used to correct any DNA mass measurement 
value, be it specific mass in picograms or a relative mass value such as a 
DNA index value. 
While a preferred embodiment of the invention has been illustrated, it will 
be obvious to those skilled in the art that various modifications and 
changes may be made thereto without departing from the scope of the 
invention as defined in the appended claims.