SIMULTANEOUS FOCUSING AND CONTOURING OF HUMAN

ZYGOTES FOR IN VITRO FERTILIZATION

Alessandro Giusti, Giorgio Corani, Luca Gambardella

Dalle Molle Institute for Artiﬁcial Intelligence, SUPSI and University of Lugano, Switzerland

Cristina Magli

International Institute for Reproductive Medicine, Lugano, Switzerland

Luca Gianaroli

INFERGEN, Lugano, Switzerland

Keywords:

In vitro fertilization, Microscopy, Phase contrast, Hoffmann modulation contrast, Segmentation, Contouring,

Autofocus.

Abstract:

Observation of ovocytes and zygotes plays an important role in In Vitro Fertilization procedures, and is usually

perfomed by means of a microscope equipped with Hoffman Modulation Contrast optics, which produces

images with a complex, side-lit, 3D-like appearance. Our algorithm operates on a Z-stack of such images taken

at different focal planes, and simultaneously identiﬁes: a) a repeatable, meaningful focal plane corresponding

to the cell’s equator line, and b) the external contour of the cell. As the cell is a thick stucture with respect to

the microscope depth of ﬁeld, the two problems are nontrivial and deeply related. Our algorithm is also robust

to other structures, clutter and artefacts affecting the images and lying at varying focal planes. We describe

implementation details, applications and experimental results of our technique.

1 INTRODUCTION

During In Vitro Fertilization (IVF) procedures, biolo-

gists observe zygotes (fertilized ova) at different times

in order to assess their quality and select the ones

maximizing the implantation success rate (Gianaroli

et al., 2007). Such observations are usually performed

by means of an inverted microscope with Hoffmann

Modulation Contrast and 20x or 40x magniﬁcation,

where focusing plays an important role as the depth

of ﬁeld is quite shallow, so that only a thin slice of

the cell’s volume is clearly visible at a single focal

plane. We solve the problem of automatically ﬁnd-

ing a repeatable and sensible focal plane from a set

of images taken at different focal planes (Z-stack), by

means of an original approach where high-level scene

information (the cell contour) is recovered simulta-

neously with the correct focal plane (see Figures 1

and 2).

Traditional autofocusing algorithms based on

global or local contrast fail at consistently ﬁnding a

meaningful and repeatable focus for the zygote ob-

servation: in fact, many images in a Z-stack exhibit

sharp, strong-contrast features. Structures of interest

such as pronuclei and polar bodies can be found at

different depths, and therefore come into focus at dif-

ferent levels (see Figure 1); a number of other struc-

tures, such as fragments and debris, are also visible at

varying depths, both inside and outside the cell, rep-

resenting strong and confusing clutter for automated

processing.

We maintain that a proper solution to the focus-

ing problem requires an higher-level understanding of

the scene. Our approach is therefore aimed at solving

both of the following problems:

• segment the zygote cell, and

• ﬁnd a focal plane where the sharpness of its con-

tour is maximized, which roughly corresponds to

the plane of the cell’s contour generator curve.

The two tasks are deeply related: in fact, the cell’s ac-

tual contour can only be recovered in an image where

it is sharply visible, and such image can only be easily

determined when the contour itself is known.

We solve both problems simultaneously by repre-

senting data from all focal planes in a single graph,

151

Giusti A., Corani G., Gambardella L., Magli C. and Gianaroli L. (2010).

SIMULTANEOUS FOCUSING AND CONTOURING OF HUMAN ZYGOTES FOR IN VITRO FERTILIZATION.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 151-158

DOI: 10.5220/0002592501510158

 SciTePress

(a) I

(b) I

(d) I

(e) I

Figure 1: The source stack is composed by a number of images (slices) taken at different focus levels. Our experiments use

23-slice stacks; 5 slices covering the whole range of a typical stack are shown in the ﬁgure. Note in-focus debris generating

strong contrast in (d,e), whereas the best focus of the cell is in (c).

then solving a minimum-cost path problem on such

graph. Sharp, in-focus edges are favored by assign-

ing a lower cost to localized, large gradient magni-

tudes. We also take advantage of the complex, 3D-

like sidelit appearance of the zygote in HMC images

for improving the system’s robustness, by enforcing

priors about the cell lighting, which is a predictable

and prominent feature. Finally, the peculiar topology

of the graph on which the minimum-cost path prob-

lem is solved implicitly enforces:

• shape priors on the cell contour, which is expected

to be smooth and circular-shaped;

• limited and smooth variations in focus along the

contour.

A similar approach has been used for robust zy-

gote segmentation from a single image in (Giusti

et al., 2009), where the cell is assumed to be already

correctly focused, which implies manual intervention

in the image acquisition process.

On the contrary, our approach removes any in-

ﬂuence of the operator in the focusing process; this

is especially important as the focal plane found by

our algorithm roughly bisects the cell in a top and

bottom half. This acts as an important reference for

determining the relative position of other structures

of interest. In addition, the knowledge of the zy-

gote contour is useful to readily compute a number

(a) I

(b) I

Figure 2: Results for the stack in Figure 1. Our algorithm

detects the contour γ of the zygote (yellow line). It is best

focused (thick line) partly in I

(lower-left part) and partly

(upper-right segment).

of quantitative measures (apparent size, simple shape

descriptors, relative positions) for the cell, which are

not easily judged otherwise. The obtained segmen-

tation and focal plane may also be applied for other

tasks, such as driving an automated microscope for

unattended imaging of zygotes, or providing a ro-

bust, precise initialization for subsequent (automatic

or user-assisted) analysis algorithms, such as those

introduced in (Morales et al., 2008; Karlsson et al.,

2005) for the segmentation of the zona pellucida.

We brieﬂy review related works in the following

Section, then introduce notation and terminology in

Section 3. Our approach is then described in detail

in section 4, and experimentally validated in in Sec-

tion 5. Section 6 concludes the paper and presents

ongoing work.

2 RELATED WORKS

Autofocus systems are widely used in microscopy,

and can be implemented either by means of additional

hardware, or by using a software approach for ana-

lyzing a sequence of camera images taken at varying

focus positions; our system belongs to the latter cat-

egory. Many approaches for software autofocus are

documented in literature (Shih, 2007), mostly based

on the maximization of local or global contrast: the

core difference of our approach is that we seek an

higher level understanding of the scene, thus provid-

ing a repeatable focusing position which is linked to a

speciﬁc focal plane which bisects the cell in a top and

bottom half. As the sample is thick with respect to

the depth of ﬁeld, and many other sources of contrast

exist in different focal planes, the same goal can not

be attained by a lower level analysis.

Our approach is based on the segmentation of the

cell contour, which by itself is not a straightforward

task even if the correct focal plane was known, due to

the complexity of the image.

Classical region-based segmentation algorithms,

including watersheds (Soille and Vincent, 1991), are

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

152

not applicable in this context because of the complex

appearance of the cell, including the surrounding zona

pellucida, clutter, and artifacts; this also hinders the

application of straightforward edge-based segmenta-

tion algorithms, as many spurious contours are de-

tected.

Iterative energy minimization methods such as ac-

tive contours (Xu, 1998) and level sets are frequently

employed in biomedical imaging: in this context,

their application is not straightforward because de-

bris are likely to generate several local minima in

the energy function, which makes quick and robust

convergence problematic; for example, in (Morales

et al., 2008) active contours are used for measuring

the thickness of the zona pellucida in embryo images,

but only after a preprocessing step aimed at removing

debris and other artifacts.

In (Beuchat et al., 2008) a semisupervised tech-

nique for measuring various zygote features is used,

where the cell shape is approximated by an ellipse:

in our case, instead, we recover the actual shape of

the cell, which is often not well approximated by an

ellipse.

The technique we are presenting includes a global

energy minimization step, and may be classiﬁed as a

specialized graph-cut (Zabih and Kolmogorov, 2004)

approach, where: a) priors on the cell shape are ac-

counted for by operating on a spatially-transformed

image and searching for a minimum-cost path on a

directed acyclic graph; b) priors on the contour ap-

pearance due to HMC lighting are directly integrated

in the energy terms; c) information at different fo-

cal planes is simultaneously represented in a single

large graph. We are therefore extending the approach

in (Giusti et al., 2009) by operating on information

from many focal planes at the same time.

Interestingly, several previous works handled the

peculiar lighting in HMC and DIC images as an ob-

stacle to segmentation (Kuijper and Heise, 2008),

and adopted preprocessing techniques for removing

it, whereas we actually exploit such appearance for

improving robustness.

3 PRELIMINARIES, MODEL AND

NOTATION

Our algorithm is designed to operate on a Z-stack

of N images taken with Hoffman Modulation Con-

trast (HMC) microscopy

. We denote the input im-

a technique delivering visually similar results is Dif-

ferential Interference Contrast (DIC), which is also a likely

application scenario for our technique.

ages as I

,...,I

, and their respective focal planes

z = z

..z

. Such focal planes can be considered

horizontal slices at different depths of a 3D space

whose cartesian axes are (x,y, z).

HMC is an imaging technique converting optical

slopes to variations of the light intensity: it is rou-

tinely used in IVF labs for observing zygotes, as it

provides a large amount of contrast for transparent

specimens and eases human observation as the objects

appear three-dimensional and side-lit, as if a light

source was illuminating them from a side (apparent

lighting direction).

The underlying imaging model is considerably

complex, especially if the effect of out-of-focus fea-

tures is taken into account. Still, several intuitive prin-

ciples hold, on which we base our approach:

• structures which lie on or near the current focal

plane z

appear sharp and exhibit strong localized

gradients in the image intensity I

;

• as the focal plane depth moves farther from the

structure’s depth, the structure image becomes

blurred. Consequently, its gradients of the struc-

ture’s image lose locality and strength, although

the global contrast and visibility of the feature

may not be affected, or may even be emphatised

in some situations

In this work, the main feature of interest is the cell

contour; as the cell is a 3D object, in order to explain

the appearance of its contour at different focus levels,

we provide the following formalization: Let S be the

surface of the cell, which we assume to be smooth,

in the 3D space (x,y,z). The contour generator curve

Γ is a curve in 3D space, identiﬁed by the locus of

points P on S such that the tangent plane to S in P

contains the z direction

. Although this deﬁnition al-

lows Γ to be composed by several disjoint curve parts,

the regularity of the cell shape, which is convex and

ellipsoid-like, allows us to assume that Γ is a single,

closed curve in the following.

We are interested in detecting the image of the

contour generator curve Γ in our input images I

. In

particular, let γ be the 2D apparent contour, i.e. the or-

thogonal projection of Γ on the (x, y) plane. Follow-

ing the principles introduced previously in this sec-

tion, a part of γ is visible and well-focused in an image

if the corresponding part of Γ is on or near the z = z

in fact, a slightly defocused feature imaged through a

phase contrast technique may appear more evident to an hu-

man operator than the same feature in perfect focus; this

makes manual focusing inherently operator-dependent and

hardly repeatable.

Note that this is similar to the concept of contour gen-

erator curve in projective geometry where an ortographic

camera is considered.

SIMULTANEOUS FOCUSING AND CONTOURING OF HUMAN ZYGOTES FOR IN VITRO FERTILIZATION

153

Figure 3: Representation of model (see text).

plane; in this case, such part of γ will exhibit large, lo-

calized gradients in image I

. The gradient intensity is

weaker as Γ gets farther away from the plane z = z

;

eventually, if a part of Γ lies far from the plane z = z

then the corresponding part of γ may be invisible (i.e.

not generating any signiﬁcant gradient) in I

Our goal is to identify γ in the (x, y) image coordi-

nates, as well as the I

image where γ is most visible,

whose focal plane z

corresponds to the depth of Γ.

As described in the following Section, we account for

the fact that different parts of Γ may lie at different

depths, by detecting different parts of γ on different I

images.

4 SIMULTANEOUS FOCUSING

AND CONTOURING OF A

ZYGOTE CELL

We divide the segmentation process in two sequential

steps: ﬁrst, we ﬁnd the approximate location (x, y) =

) of the cell center; in doing this, we assume

that a single zygote is visible in the image, which is

always the case as zygotes are kept in separate wells

in clinical practice.

Then, we build a transformed representation of

the whole Z-stack in polar coordinates, constructing

a single graph, then using a minimum-cost path for-

mulation in order to recover the actual zygote contour

and its focus plane.

We brieﬂy introduce the former part, which we

consider of lesser importance and interest, in Section

4.1. The main focus is instead on the latter part, de-

scribed in Section 4.2.

4.1 Approximate Localization of Zygote

Center

First, a representative image for the whole stack is se-

lected, by applying a naive autofocusing algorithm: in

particular, for each slice i we compute a number f (i)

as the average value for the modulo of the gradient of

. We consider the slice b maximizing such value:

b = argmax

( f (i)). (1)

Due to the gradient in the internal part of the

cell, and strong gradients due to debris and additional

structures, I

does not represent in general the slice

where γ is most visible (see Section 5). However, it

proves to be a suitable image for applying the same al-

gorithm described in (Giusti et al., 2009) for roughly

detecting the cell centroid.

In particular, in order to ﬁnd an approximate loca-

tion for the cell centroid, the modulo of the image gra-

dient of I

is subsampled to a smaller image, which is

automatically thresholded then regularized by means

of median ﬁltering (see Figure 4 a,b). The largest con-

nected component is isolated and its holes ﬁlled (Fig-

ure 4 c); for each point inside the resulting region,

the minimum distance to the region boundary is com-

puted by means of the distance transform; the point

with the maximum distance is ﬁnally chosen as the

approximate centroid of the cell.

This preliminary analysis phase is not critical for

the quality of results, as the subsequent processing

tolerates quite large displacements of the detected

centroid; nonetheless, this simple algo- rithm counter-

intuitively proves to be quite robust also in presence

of large artifacts attached to the cell; this is mainly

due to the distance transform, which implicitly can-

cels or reduces the effect of any non-convex artifact

protruding of the border of the cell.

4.2 Contouring and Focusing of the

Zygote

4.2.1 Transformation to Polar Coordinates

Once the zygote centroid (c

) is detected in one

slice, it is considered valid in all the images of the

stack (as no signiﬁcant displacements happens be-

tween focus levels). For each image I

, a circular

corona centered at such point is transformed using

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

154

(a) (b) (c) (d) (e)

Figure 4: Approximate localization of the cell center. (a): original image. (b): binary mask obtained after thresholding the

modulo of the gradient. (c): largest connected component with holes ﬁlled. (d): distance transform. (e) the maximum of

the distance transform is considered as the approximate center of the cell. Note that the large artifact on the left does not

signiﬁcantly displace the maximum of the distance transform.

bilinear interpolation to an image J

in polar coordi-

nates:

(θ,ρ) = I

+ ρcos(θ),c

+ ρsin(θ))

0 ≤ θ < 2π ρ

≤ ρ ≤ ρ

i = 1 · · · N.

(2)

In order to account for variations in the cell shape

and errors in the centroid location, the range [ρ

÷ ρ

]

of ρ values is very conservatively set to [0.3r,1.5r],

where r represents the expected cell radius; this is a

quite large range (see Figure 4e), which allows for

large variations in the actual radius of the zygote, and

for displacements of the estimated centroid (c

ρ and θ values are uniformly sampled in ρ

and θ

intervals, respectively, which correspond to rows and

columns of each image J

. We use ρ

= 80, θ

= 180

in the following.

4.2.2 Computation of Energies

Images J

are then processed in order to associate an

energy to each pixel for each of the N planes. Such

energy will drive the following graph-based formula-

tion. Let α be the direction of apparent lighting due

to HMC, which only depends on the optical setup and

can be assumed known in most scenarios (if it’s not,

it can be easily estimated); we deﬁne an energy E

for

each pixel of J

, regardless on its plane in the stack,

as:

(θ,ρ) = P



z }| {

cos(θ − α) · G

z }| {

sin

(θ − α) ·



)



(3)

P(x) =



1 + e



−1

(4)

where G

denotes the gradient operator along the ρ

axis, and P(·· · ) is a simple decreasing sigmoid func-

tion which conditions the energy values to lie in the

[0 ÷ 1] interval; the scaling parameter k is not critical,

and can be safely set to 1/5 of the image’s dynamic

range.

The ﬁrst term in (3) dominates where the con-

tour is orthogonal to the apparent light direction,

i.e. where the cell is expected to appear signiﬁ-

cantly lighter (θ − α ' 0) or darker (θ− α ' ±π) than

the surroundings; large gradient values with a sign

consistent with this assumption lead to lower ener-

gies. The second term takes account for the unpre-

dictability of the contour appearance where the con-

tour is parallel to the apparent light direction, and

just associates lower energies to large absolute values

for G

4.2.3 Building of a Directed Acyclic Graph

A single directed acyclic graph is built over the stack

of all J

images, by instantiating a node for each pixel

and for each plane (for a total of ρ

· θ

· N nodes).

Moreover:

• arcs are added connecting each node to its three

8-neighbors at the right on the same plane;

• every β

columns, a set of interfocal arcs are

added. In particular,

The cost of each arc is set to the energy E

(θ,ρ) of its

source node.

A single global source node s is added, with zero-

cost arcs that lead to every pixel in the ﬁrst column

of every plane; also, we add a sink node reached by

zero-cost arcs from every pixel at the last column of

every plane.

4.2.4 Minimum-Cost Path

As the resulting graph is a directed acyclic graph,

efﬁcient algorithms are available for computing the

minimum-cost path from the source node to the sink

node. Such path passes through low-energy arcs, and

is constrained by the graph topology to have a quite

regular shape, because:

SIMULTANEOUS FOCUSING AND CONTOURING OF HUMAN ZYGOTES FOR IN VITRO FERTILIZATION

155

Figure 5: The stack of I

slices (a) is transformed to J

slices in polar coordinates (b), then an energy value E

for each pixel and a graph is built on each (d). A single global graph is then computed.

• the path is forced to steadily move from left to

right (i.e. increasing θ values);

• variations along the radial direction ρ are bounded

in slope;

• shifts to an adjacent focal plane can only occur

rarely (e.g. every β

columns).

After excluding the source and sink nodes (which

have no geometric meaning), the path can be brought

back to cartesian coordinates by using the inverse

transform to (2): the result is a curve in the (x,y, z)

space. If the ﬁrst and last nodes of the path (almost)

match, the curve can be smoothly closed. If such

points do not match, we have a strong hint that the

image did not contain a zygote, or that the approx-

imate center was very displaced with respect to the

true center of the zygote; we are currently disregard-

ing this possibility as none of our test images exhibits

this issue, although we plan to investigate the related

problem of the zygote detection in future works.

In practice, the resulting path estimates the 3D

contour generating curve Γ. It simultaneously rep-

resents the contour of the cell and identiﬁes its main

focal plane: the interior of the resulting polygon pro-

jected to the 2D plane (x,y) deﬁnes the computed bi-

nary mask M. The average value for the focus plane

associated to the nodes in the minimum-cost path de-

ﬁnes the focus plane z

where the cell contour is

sharpest.

Larger values for ratio θ

/ρ

, as well as a smaller

parameter, allow more freedom to the path built

over the graph, which translates to better accommoda-

tion of an irregular cell shape or a displaced centroid

); at the same time, this reduces the robustness

of the approach, as shape priors are less strongly en-

forced. We found any ratio between 1.5 and 3.0 to be

acceptable, although we keep with θ

/ρ

= 180/80 =

2.25 in the following; changing β

has little effect in

most cases, and should be set such that the ratio θ

/β

is larger than 8 – i.e. there are at least 8 places in the

graph where the interfocal arcs are created – which

allows the resulting contour generator curve to span

at most 4 adjacent focal planes. Due to the almost

spherical shape of the zygotes, none of our test im-

ages required a larger variation

5 EXPERIMENTAL RESULTS

We evaluated the technique on 101 image stacks rep-

resenting 84 unique zygotes from 22 different patients

(some zygotes are acquired twice). Stacks are ac-

quired with a 0.35 megapixel JVC camera attached

to an Olympus IX-51 inverted microscope equipped

with a 20x objective, HMC optics, and a 0.63x cam-

era adapter. Each stack is composed by 23 images,

acquired in a rapid sequence (10 frames per second)

during a regular motion of the focus knob; due to the

manual nature of the stack acquisition, the spacing be-

tween adjacent slices of a stack is not exactly ﬁxed

but consistently averages between 8 and 12 microns;

the whole stack covers approximately 200 microns,

which amounts at twice the expected diameter of the

cell. The contour generator curve Γ of the cell is al-

ways included in the range of focus, although not in

a predictable position. The cell is always completely

included in the image, but is not well centered in the

image as the biologist acquiring the images uses the

microscope oculars and thus does not have a reference

for the center of the image acquired by the camera.

We can now quantitatively evaluate the effective-

ness of our focusing technique. We compare z

, the

average focus plane of Γ determined by our algorithm,

to a reference focus depth z

ref

, obtained by an operator

carefully navigating the stack and choosing the slice

maximizing the overall contrast and precision of the

cell contour. We consider z

ref

as the ground truth.

We also compare two additional estimators for

ref

• z

quick

, obtained by an operator rapidly focusing a

stack (in about 2 seconds on average) by means

note that if the cells were perfectly spherical then Γ

would lie on a single focal plane

note that ﬁxed spacing is never assumed in our ap-

proach; still, equally-spaced slices, which we plan to ac-

quire by means of automated microscopes, will prove very

useful for performing 3D measurements on the resulting

stacks

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

156

(a) (b) (c) (d)

Figure 6: Some images focused and simultaneously segmented by our approach. γ shown in yellow. Thin white lines delimit

the circular corona around the detected approximate center (c

Table 1: Evaluation of focusing performance (see main text). First row reports the error in terms of number of z-stack slices;

second row expresses the approximate value in µm, assuming a ﬁxed distance between adjacent slices of 10µm.

quick

autofocus

RMSE [slices] 1.26 3.22 3.90

RMSE [µm] 12.6 32.2 39.0

Table 2: Evaluation of segmentation performance (see main text).

q d

pixels fraction of R pixels fraction of R

0.949 2.625 0.020 5.823 0.044 0.017 0.077

of the mouse wheel, with the goal of getting an

acceptable contrast.

• z

autofocus

, the measure computed by the naive auto-

focusing algorithm which maximizes the average

modulo of the gradient in the image; we use such

measure in order to ﬁnd the approximate cell cen-

ter (see Section 4.1). We evaluated other simple

histogram-based autofocusing algorithms, which

performed consistently worse.

The root mean squared error (RMSE) with respect

to z

ref

, computed on all 101 stacks, is reported in Ta-

ble 1 for each of z

, z

quick

, and z

autofocus

On average, the contour generator curve spanned

a range of 1.71 slices (u 17µm).

In order to evaluate the quality of the segmenta-

tion, we randomly selected 40 of the 101 stacks and

created a ground truth binary mask T for each, by con-

sidering the slice with best focus as returned by our al-

gorithm and manually segmenting the contour of the

zygote on such slice. We then compared this ground

truth value to the segmentation returned by algorithm,

represented by a binary mask M, by considering:

• the Jaccard quality metric q =

|T ∩M|

|T ∪M|

0 ≤ q ≤ 1,

which approaches 1 for better segmentations;

• the average distance d

and maximum distance d

between the true boundary and the computed one;

• the relative error in the measured area e

;

• the absolute error in the measured eccentricity e

The results, shown in Table 2, are consistent with

those reported in (Giusti et al., 2009), where a sim-

pliﬁed version of the same algorithm is applied to one

single image with the contour in good focus.

6 DISCUSSION, CONCLUSIONS

AND RELATED WORKS

We presented an effective technique for simultane-

ously focusing an human zygote cell and recovering

its contour. We maintain that, in this scenario, the

problem of precise and repeatable focusing is strictly

tied to the segmentation problem, as low-level fo-

cus measures which consider the image as a whole

are easily misled by additional structures and debris

which exhibit strong contrast. Our approach segments

the cell and determines its focal plane in a single step,

by operating on a single graph summarizing informa-

tion from all the slices in the stack.

We have shown that our focusing algorithm is able

to determine a repeatable focal plane, which has a

SIMULTANEOUS FOCUSING AND CONTOURING OF HUMAN ZYGOTES FOR IN VITRO FERTILIZATION

157

meaningful interpretation as the average depth of the

contour generator curve of the cell. The precise and

automated measurement of such depth is a fundamen-

tal step for providing accurate and repeatable mea-

sures of the zygote morphology which also account

for 3D information. Obviously, this requires operat-

ing on stacks whose slices have a known depth, which

we are going to obtain in the future. We are currently

improving the system by automatically segmenting,

measuring and locating in 3D other zygote structures

such as the pronuclei. This has important applications

in the clinical practice for In Vitro Fertilization.

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