On Benchmarking Cell Nuclei Segmentation Algorithms for
Fluorescence Microscopy
Frederike Wirth, Eva-Maria Brinkmann and Klaus Brinker
Hamm-Lippstadt University of Applied Sciences, Marker Allee 76-78, 59063 Hamm, Germany
Keywords:
Medical Image Analysis, Computer Vision, Fluorescence Microscopy, Cell Nuclei Segmentation,
Segmentation Benchmarking, Watershed Segmentation.
Abstract:
Cell nuclei detection is an essential step in the context of many image analysis tasks related to microscopy
images and therefore also plays a role in highly topical fields of research like the development of personalised
immunotherapy approaches against several types of cancer. Motivated by this observation, a whole zoo of ad-
vanced methods to accomplish cell nuclei segmentation has been proposed in recent years. This development
in turn stresses the need to set up a well-justified and reproducible standard of comparison for the evaluation of
these sophisticated approaches. In this paper, we describe how such a reference framework based on standard
seeded watershed segmentation for fully automatic cell nuclei detection can be obtained. In particular, we
provide a detailed review of a publicly available dataset, give a detailed account of the methods and evaluation
measures we consider to enable the highest possible reproducibility of our results and discuss the suitability
of different variants of seeded watershed segmentation for the mentioned purposes.
1 INTRODUCTION
In 2018, the Nobel Prize in Physiology or Medicine
was awarded to the two immunologists J. P. Allison
and T. Honjo crediting their seminal biochemical dis-
coveries that laid the foundation for new approaches
to cancer therapy using so-called immune checkpoint
inhibitors (ICIs). In recent years, this novel type of
treatment led to groundbreaking advances in the fight
against cancer, where the general idea underlying any
immunotherapy approach is to activate the patient’s
immune system to attack tumour cells and ideally kill
the cancer (cf. e.g. (Kelly, 2018)). While first long
term studies (cf. e.g. (Schadendorf et al., 2015; Anto-
nia et al., 2017)) corroborate that for various types of
cancer an immune checkpoint inhibitor-based therapy
may lead to a significantly longer progression-free
survival (PFS) and higher 5-year overall survival (OS)
rates, it is still an open research question to predict
which patients will actually benefit from this novel
cancer immunotherapy or might encounter severe side
effects.
In order to make a contribution to a more person-
alised medical treatment in this area, we plan to build
a predictive model based on fluorescence microscopy
imaging using multiple biomarkers. Within the over-
all processing pipeline, the first step is to detect the
cell nuclei. Hence, the aim of this paper is to set up
a reference framework for cell nuclei segmentation.
Relying on well-established standard methods, vari-
ants of watershed segmentation, to be precise, that we
apply to publicly available cell image data, we cre-
ate a reliable benchmark for the evaluation of more
sophisticated segmentation algorithms. These consti-
tute the starting point to create a personalised treat-
ment plan.
The remainder of this paper is organised as fol-
lows: First, we describe the two datasets that we con-
sider throughout this work and explain how this data
can be converted into a ground truth segmentation for
later evaluation. Subsequently, we recall the general
idea of the watershed segmentation method and give
a detailed account about our implementation of sev-
eral variants of this well-established approach. Even
more, we recapitulate the definition of common per-
formance measures. Based on these measures as well
as a visual inspection of the segmentation results, we
eventually compare the previously described variants
of watershed segmentation, thus providing a repro-
ducible evaluation baseline for more involved seg-
mentation approaches.
2 THE DATASET
In the following, we will start with a description of
the used dataset including basic information collected
in a manual review of the data. Afterwards, we will
164
Wirth, F., Brinkmann, E. and Brinker, K.
On Benchmar king Cell Nuclei Segmentation Algorithms for Fluorescence Microscopy.
DOI: 10.5220/0008967901640171
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING, pages 164-171
ISBN: 978-989-758-398-8; ISSN: 2184-4305
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

explain how to convert the images into a more ap-
propriate representation. All cell lines and a hand-
labelled image collection were published by the group
of R. F. Murphy
1
in order to facilitate further research
(cf. also (Coelho et al., 2009).
2.1 Data Review
The dataset that is used in this paper contains two
cell lines, U20S and NIH3T3. The U20S cell line
was originally created and used by Peng et al. for
the development of a pattern unmixing algorithm
(Peng et al., 2010). The images contained in this
cell line show human osteosarcoma cells dyed with
Hoechst 33342 (Peng et al., 2010). Recalling that
in our previously sketched overall research project
we are interested in the analysis of fluorescence mi-
croscopy images of human cancer cells, the images
of the U20S cell line seem particularly appropriate as
a preparation for further work in the context of this
project. The origin of the NIH3T3 cell images, is a
collection using the method of Osuna et al. (Osuna
et al., 2007). These mouse embryo cells where cul-
tured by Todaro and Green (Todaro, 1963) and also
dyed with the fluorescence stain Hoechst 33342 (Os-
una et al., 2007). Looking at the U20S cell line, we
notice that some of the cells are clustered, which ren-
ders the segmentation of single cell nuclei particularly
challenging. The number of cells per image varies be-
tween 14 and 43. Some of the images contain bright
artefact spots as part of the background. The mean
brightness of the cells shown in the images of the
NIH3T3 cell line is lower than in the first image col-
lection. In addition, the former contains even brighter
artefacts than the U20S images. We also note that
the cells of the NIH3T3 line are less clustered and the
number of cells varies between 18 and 56.
2.2 Data Conversion
Beside the fluorescence microscopy nuclei images
(50 per cell line), the data also contains five images
per cell line that are hand segmented by A. Shariff
as well as 48 images and 49 images, respectively,
that are hand segmented by L. P. Coelho. The three
missing images were rejected by L. P. Coelho as be-
ing not in-focus. The hand segmented masks were
provided as the original microscopy image with the
cell contours marked in red. However, to compute
further evaluation measurements, it is more appro-
priate to represent the images in a manner such that
the background is labelled as zero and every cell has
1
http://murphylab.web.cmu.edu/data
a unique integer label. Therefore, a flood fill algo-
rithm was used to convert the cell boundary images.
Care must be taken because some of the borders do
not completely surround the cells, so the flood fill al-
gorithm might consider these cells to be part of the
background. This problem can be solved by an accu-
rate review of the data, where existing holes in the cell
boundaries are manually closed. Another difficulty of
the automatic image conversion are tiny artefacts with
a size of one pixel that might be counted as a single
cell. To avoid these small cells to distort the results,
they can also be manually deleted or removed by an
automatic small object filter, as it is e.g. provided by
scikit-image
2
. It is important to note that there are dif-
ferences between the reference images manually seg-
mented by L. P. Coelho and the ones segmented by
A. Shariff. Those discrepancies are i.a. caused by the
way cells touching the border of the image are han-
dled. As a consequence, we decided to delete every
border cell to avoid any ambiguities.
3 SEGMENTATION METHODS
As mentioned before, the goal of this paper is to de-
scribe the creation of an evaluation baseline for the
future assessment of state-of-the-art segmentation ap-
proaches with a particular eye on cell nuclei detection
in fluorescence microscopy images.
In the context of benchmarking, a key point is the
easy reproducibility of results. Hence, we decided to
only draw on standard methods, for which fast and re-
liable implementations in widely-accessible computer
vision libraries such as OpenCV or scikit-image exist.
A well-established class of approaches that not only
satisfy this criterion but, in addition, are known to be
able to separate touching and overlapping objects (cf.
(Russ and Neal, 2015, p. 483) and references therein)
rendering them particularly suited for the detection of
clustered cells in microscopy images are segmenta-
tion algorithms that are based on the so-called water-
shed transformation.
3.1 Watershed Transformation
An important first step for understanding the wa-
tershed transformation in image processing is a re-
interpretation of a given single-channel image as a
topological relief with the intensity values being re-
garded as physical elevation. Then, the central idea
of the watershed transformation first introduced as a
morphological tool in (Digabel and Lantu
´
ejoul, 1978)
2
https://scikit-image.org/ (version 0.15.0)
On Benchmarking Cell Nuclei Segmentation Algorithms for Fluorescence Microscopy
165

Figure 1: Schematic representation of segmentation pipeline based on the watershed transformation.
and made applicable to digital images by Vincent and
Soille (Vincent and Soille, 1991) can be regarded as
the straightforward adaptation of the concept of wa-
tersheds in geography to this setting. Watersheds are
the lines that separate adjacent so-called catchment
basins, the latter describing the area of land from
which water flows to a deeper water reservoir like a
river, a lake or an ocean.
Given the above identification of image and topo-
logical relief, the characterization of catchment basins
and watersheds is a means to assign every pixel in the
image either to a specific region or to a line that sep-
arates two regions. In order to determine the catch-
ment basins, we now imagine that the relief is grad-
ually filled with water. More precisely, we assume
that starting from the lowest points of the relief, i.e.
the smallest intensity values of the image, the water
pours out through local minima, which in this con-
text are commonly referred to as seeds. By this grad-
ual immersion of the relief more and more catchment
basins around the seeds emerge and grow, this way it-
eratively assigning adjacent pixels to regions. When-
ever the catchment basins flooded by two different
seeds are about to fuse, the contact pixels are labelled
as watershed lines such that we finally obtain a full
decomposition of the image into various regions of
cohesive pixels separated by closed contours. As a
consequence, the watershed transformation is often
applied in the context of image segmentation.
3.2 Implementation
In order to create a baseline for new cell segmenta-
tion algorithms, we implemented a seeded watershed
pipeline that is based on methods of the following
standard python libraries: NumPy
3
, OpenCV
4
, scikit-
image
5
. Figure 1 illustrates the process of this seg-
mentation pipeline that starts with an optional prepro-
cessing of the input image before the seed detection
takes place, which is based on the computation of re-
gional maxima. The latter is defined as a pixel with an
intensity value v that is not directly located next to a
pixel with an intensity value higher than v. Addition-
ally, it is not connected to such a pixel through chain-
ing pixels with an intensity value equal to v. The input
image of the watershed method might also be prepro-
cessed either with a Gaussian blur or with a distance
function as proposed in a more recent online tutorial
of L. P. Coelho.
6
The watershed transformation is not
able to distinguish between cells and the image back-
ground. As a consequence, it is necessary to subtract
a mask created with the mean threshold afterwards.
The mask is not generated with the original image but
with a blurred version of it. Finally, we applied some
postprocessing steps to this mask. By analogy with
the conversion of the ground truth data, we deleted all
3
https://numpy.org (version 1.16.5)
4
https://opencv.org (version 3.4.2)
5
https://scikit-image.org/ (version 0.15.0)
6
https://github.com/luispedro/python-image-tutorial/
blob/master/Segmenting%20cell%20images%
20(fluorescent%20microscopy).ipynb
BIOIMAGING 2020 - 7th International Conference on Bioimaging
166

Table 1: Overview of the various implemented variants for watershed segmentation considered in this paper (cf. also Figure 1).
variant of watershed
segmentation
1
σ-value for seed
computation
2
watershed input
image
3
threshold for binary
mask creation
variant 1 12 original intensity image 12
variant 2 12 blurred intensity image
(σ = 6)
12
variant 3 19 original intensity image 12
variant 4 19 original intensity image 4
variant 5 19 threshold with t
mean
of
blurred intensity image
(σ = 2), then distance
transform
4
boundary cells from the segmentation masks. More-
over, we applied an object filter that removes all ob-
jects smaller than the smallest cell of the respective
cell line in the reference dataset.
Looking at the full segmentation pipeline (see Fig-
ure 1), there are three possible points of adjustment:
the value of σ controlling the amount of Gaussian blur
in the image used for computing the seeds (see num-
ber 1 in Figure 1), the preprocessing steps of the wa-
tershed input image (see number 2 in Figure 1) and
also the σ-value of the Gaussian blur for the creation
of the threshold mask (see number 3 in Figure 1).
We consider the following segmentation pipelines
as summarised in Table 1: Variant 1 is an imple-
mentation of the settings proposed by Coelho et al.
(Coelho et al., 2009). The watershed transformation
of variant 2 works on a blurred version of the cell im-
ages. In variant 3, the σ-value that controls the blur-
ring of the original input image before the maxima de-
tection is increased, which lowers the absolute num-
ber of watershed seeds. Variant 4 is based on the same
generation of seeds but the σ-value used for the cre-
ation of the binary mask is decreased. The watershed
input image of variant 3 and 4 is the original image.
For variant 5 the input of the watershed image is a
distance transform of the thresholded binary image,
where each pixel is assigned the shortest (Euclidean)
distance to the background as labelled by the binary
mask.
4 EVALUATION
Regarding the manual segmentations by L. P. Coelho
as ground truth, we can compare the performance of
the various variants of watershed segmentation ex-
plained above by employing several well-known su-
pervised evaluation methods. These approaches can
be categorised in pixel-based and object-based meth-
ods. The Rand Index (RI) (Rand, 1971) and the Jac-
card Index (JI) (Jaccard, 1901) both belong to the for-
mer class. They compare the assignment of every
pixel in the segmented and reference image. Their
disadvantage is that they take no account of informa-
tion about the location of the cell border. That is why
we additionally calculate the Hausdorff Distance as
described by Chalana and Kim (Chalana and Kim,
1997, and references therein)) as well as the Nor-
malised Sum of Distances (NSD) proposed by Coelho
et al. (Coelho et al., 2009). A third group of evalua-
tion measures are the four error counting values split,
merged, added and missing. They refer to the mis-
takes in the cell images that are intuitively seen by a
human observer and have been used in similar works
as e.g. (Baltissen et al., 2018; Coelho et al., 2009).
Here and in the following, R denotes the reference
image, i.e. the ground truth, and S refers to the asso-
ciated segmented image created with one of the men-
tioned algorithms.
4.1 Rand Index and Jaccard Index
The Rand Index (Rand, 1971) and the Jaccard Index
(Jaccard, 1901) were developed for the evaluation of
clusterings. In a certain sense, they both measure to
what extend the reference image and the segmented
image agree about pixels being assigned to the same
object or different objects. Let S
i
be the intensity
value of a pixel in the segmented image S and S
j
be
the intensity value of a second pixel in S. Analo-
gously, R
i
and R
j
are the intensity values of a pixel
pair in the reference image with the same coordinates
as S
i
and S
j
. We consider every possible combination
of two different pixels, disregarding order, i.e. every
combination of S
i
and S
j
with i 6= j in the segmented
image. All these possible pixel pairs are then cate-
gorised in four groups, labelled as A, B, C and D:
On Benchmarking Cell Nuclei Segmentation Algorithms for Fluorescence Microscopy
167

• A: S
i
= S
j
and R
i
= R
j
,
• B: S
i
6= S
j
and R
i
= R
j
,
• C: S
i
= S
j
and R
i
6= R
j
,
• D: S
i
6= S
j
and R
i
6= R
j
.
In other words, the categories A and D include every
pixel pair, where the two segmentations agree about
assigning both pixels to the same object or not. On
the contrary, B and C contain the pixel pairs where the
reference and the segmented image disagree about the
pixels at position i and j being part of the same object.
Moreover, we let a, b, c and d denote the number of
pixel pairs in the categories A, B, C and D, respec-
tively.
Given these notations, the Rand Index (RI) is de-
fined as the quotient
RI(R, S) =
a + d
a + b + c + d
=
a + d

n
2

, (1)
where n denotes the total number of pixels either in
the segmented image S or the reference image R. A
Rand Index of 1.0 implies a complete overlap of the
objects in R and S, while a small overlap between the
segmented and the reference regions corresponds to a
lower Rand Index.
The Jaccard Index (JI) given by
JI(R, S) =
a + d
b + c + d
(2)
is based on the same classification of pixel pairs as ex-
plained above. The upper limit of the Jaccard Index
depends on the number and size of cells in compari-
son to the image size. Consequently, it is only appro-
priate to compare Jaccard Indices taken from results
created with identical image data.
4.2 Hausdorff Distance and Normalised
Sum of Distances
The pixel-based metrics have a serious disadvantage:
they are quite sensitive to small variations of the bor-
der position. Other well-known segmentation metrics
deal with this problem by using the distance between
the reference and the segmented borders as a measure.
The Hausdorff Distance (HD) proposed by Bamford
(Bamford, 2003), is defined as the maximum of the
set of shortest distances between two shapes, or as
described by Coelho et al. (Coelho et al., 2009)
HD(R, S) = max{D(i) : S
i
6= R
i
}, (3)
where D(i) is the distance of every pixel to the bor-
der of the reference object. The Hausdorff Distance is
calculated for every segmented object and its assigned
reference object. To aggregate the results for the en-
tire image, the average Hausdorff Distance of all ob-
jects is calculated. A similar approach is the Nor-
malised Sum of Distances (NSD) which is described
as
NSD(R, S) =
∑
i
JR
i
6= S
i
K ∗ D(i)
∑
i
D(i)
, (4)
where D(i) is again the distance of every pixel to the
border of the reference object (Coelho et al., 2009). In
the same manner as described above for the Hausdorff
Distance, we then calculated the average NSD value
of all segmented objects.
4.3 Error Counting Metrices
The error counting metrics split, merged, added and
missing belong to the class of object-based measure-
ments and depend on the detection of objects in the
two images. As a preparation step, every object of S
is assigned to an object in R. To this end, for each
object in S, the object in R that has the highest num-
ber of corresponding pixels is identified and its la-
bel is saved in the list assignments, where this object
might also be the background of R. Similarly, a list
reverse assignments is created, where now every ob-
ject of R is assigned to an object of S. Then, the error
counting metrics are defined as:
• Split: The number of objects in R, where two or
more objects in S are assigned to only one object
in R.
• Merged: The number of objects in S, where two or
more objects in R are assigned to only one object
in S.
• Added: The number of objects in S that are as-
signed to the background of R.
• Missing: The number of objects in R that are as-
signed to the background of S.
Given these definitions of the metrics, it is possible
that a cell could be counted as missing, although it is
only much smaller than it should be, because in this
case the reference cell is assigned to the background.
The other way round, cells with a considerably too
large size might be identified as added objects.
5 DISCUSSION
If we want to compare algorithms for automatic seg-
mentation in experimental data like fluorescent mi-
croscopy images of cells, we always face the problem
that no actual ground truth is available. As a con-
sequence, it is not a-priori clear what the ’right’ re-
sult should look like. Thus, it is a common approach
BIOIMAGING 2020 - 7th International Conference on Bioimaging
168

Table 2: Summary of the quantitative evaluation measures computed for the segmentation results obtained by the various
approaches considered in this paper (cf. also Figure 1 and Table 1).The left number always refers to the average value over
all images of the respective measure for the U20S cell line, while the right number corresponds to the NIH3T3 cell line.
pipeline RI (in %) JI HD NSD split merged added missing
manual 96.9 / 94.5 3.0 / 4.0 12.7 / 14.8 0.2 / 0.7 0 / 0 0 / 0.2 0.2 / 0.2 0 /2
variant 1 92.1 / 82.7 2.3 / 2.0 36.4 / 18.6 3.9 / 1.7 12.0 / 1.1 0.1 / 0.9 3.3 / 7.9 0.9 / 4.9
variant 2 91.6 / 82.7 2.2 / 2.0 36.7 / 18.1 4.0 / 1.7 12.4 / 1.1 0.1 / 0.9 4.1 / 8.1 0.9 / 4.8
variant 3 92.3 / 83.1 2.3 / 2.1 16.5 / 17.3 0.9 / 1.4 1.0 / 0.0 0.5 / 1.5 1.5 / 5.9 1.0 / 5.4
variant 4 97.0 / 86.5 2.7 / 2.3 13.9 / 16.2 0.6 / 1.0 1.0 / 0.0 0.5 / 1.6 1.5 / 5.4 0.5 / 4.0
variant 5 97.0 / 88.2 2.7 / 2.5 13.2 / 16.7 0.5 / 1.2 0.7 / 0.1 0.6 / 1.7 1.5 / 4.3 0.4 / 5.7
Figure 2: Close-up of the processed cell images. From left to right: Cell boundaries created with variant 1. Cell boundaries
created with variant 2. Watershed transformation created with variant 1. Watershed transformation created with variant 2.
to rely on a manual ’expert’ segmentation. However,
since the latter is based on human experience, results
may vary significantly between different manual seg-
mentations (inter-observer variability), in particular
since fluorescence microscopy images are typically
characterised by low contrast, blur and other artefacts
(cf. also (Sonka et al., 2014, p. 241)). These discrep-
ancies are reflected by the numbers given in the first
line of Table 2, which correspond to a comparison of
the manually segmented images of A. Shariff and the
respective manual segmentations of L. P. Coelho. In
particular, these numbers indicate that we cannot ex-
pect any result obtained by an automatic segmentation
routine to perfectly agree with the manual reference
segmentation. After this preliminary remark, let us
now briefly discuss the results of the various variants
of watershed segmentation.
The first two variants of watershed segmentation
we consider in this paper are based on the seeded
watershed segmentation method described in (Coelho
et al., 2009), where both the seed computation as well
as the binary mask creation were carried out for a
blurred version of the intensity image with σ = 12 as
mentioned by the authors. The only difference be-
tween these versions is the image to which the water-
shed transformation (for fixed seeds) is applied. In the
first case we apply it to the original intensity image,
while in the second case a blurred version of the in-
tensity image is used (cf. Table 1). We implemented
both variants, since from our point of view the de-
scription in (Coelho et al., 2009) was not completely
explicit in this respect, where regarding the evaluation
in Table 2 the difference in the segmentation result
anyway seems to be marginal. This impression is fur-
ther confirmed by the images in Figure 2. Here, the
main difference appears to be that in the latter variant
the watersheds separating the catchment basins fol-
low a smoother course. Given these observations, we
decided to not pursue the latter approach further but
rather apply the watershed transform to the original
intensity image to keep the complexity of the segmen-
tation pipeline and the number of parameters as low
as possible.
Looking again at the images in Figure 2, we more-
over recognise that for both variants the upper cell in
this close-up is split into two, an indicator that these
variants of watershed segmentation have a tendency
towards oversegmentation. Again this observation is
in line with the numbers given in line two and three
of Table 2, since the number of splits is far greater
than the number of merged cells. Accordingly, we
increased the value of σ (cf. number 1 in Figure 1),
i.e. the amount of blur, which reduced the number
of local minima and thus the number of seed points
as illustrated by the two latter images in Figure 3.
Note that we only increased the σ-value for the seed
computation, but left the σ for the binary mask cre-
ation untouched. As can be seen in the fourth line
of Table 2, this slight modification indeed resulted in
a considerably reduced amount of split cells (an ef-
fect that is also reflected by the first two images in
Figure 3), while on the other side of the coin the num-
ber of merged cells slightly increased. However, since
the Rand Index and the Jaccard Index remained on a
similar level, while the Hausdorff Distance as well as
the NSD notably decreased, we conclude that all in all
On Benchmarking Cell Nuclei Segmentation Algorithms for Fluorescence Microscopy
169

Figure 3: Close-up of the processed cell images. From left to right: Cell boundaries created with variant 1. Cell boundaries
created with variant 3. Seed points created with variant 1. Seed points created with variant 3.
Figure 4: Close-up of the processed cell images. Left images: example of cell boundaries created with variant 3 and variant 4,
respectively. Right images: another example of cell boundaries resulting from variant 3 and variant 4, respectively.
Figure 5: Close-up of the processed cell images. Left images: example of cell boundaries created with variant 4 and variant 5,
respectively. Right images: another example of cell boundaries resulting from variant 4 and variant 5, respectively.
the reduction of seed points resulted in a better overall
segmentation performance.
Taking another look at the second image in
Figure 3, a remaining deficiency of the result pro-
vided by the third segmentation approach is that the
red contour marking the detected object still encir-
cles an area that is significantly larger than the ac-
tual cell. In order to address this issue, we conceived
yet another version of watershed segmentation, where
in comparison to the previous variant, we decreased
the σ-value for the binary mask creation. Optimisa-
tion of this second σ-value just seemed to be the log-
ical next step, since our segmentation pipeline any-
way already included the computation of two differ-
ent blurred versions of the original intensity image.
As before, the optimisation of σ was carried out with
respect to the first image and subsequently the seg-
mentation pipeline with this parameter choice was ap-
plied to the entire sequence. In view of the images
provided in Figure 4 and with regard to the numbers
given in the fifth line of Table 2, we conclude that
the latter version of watershed segmentation indeed
yields rather convincing results for the given data set
that could serve well as a baseline for the compara-
tive assessment of more recent and sophisticated fully
automatic segmentation approaches.
The last version of watershed segmentation that
we evaluated in this paper differs from the previously
described version by the input of the watershed trans-
form: the original intensity image is replaced by a dis-
tance transform of the latter. This version of seeded
watershed has e.g. been employed in a more recent
work of L. P. Coelho
7
and seems particularly suited
for roundish objects, since in this case it might pre-
vent oversegmentation as it is exemplified by the first
two images in Figure 5. However, we also found cases
like the one shown in the latter two images of Fig-
ure 5, where the distance transform-based watershed
segmentation merged two cells that were separated
by the previously discussed segmentation pipeline.
With regard to the numbers given in Table 2, we see
that all in all the evaluation measures indicate that on
average the last two approaches seem to yield seg-
mentation results of similar quality even though they
both have their merits and demerits leaving room for
7
https://github.com/luispedro/python-image-tutorial/
blob/master/Segmenting%20cell%20images%
20(fluorescent%20microscopy).ipynb
BIOIMAGING 2020 - 7th International Conference on Bioimaging
170

improvements by more advanced segmentation ap-
proaches. From our experience, we thus conclude
that the latter two variants of watershed segmentation
might both serve well as an evaluation baseline for the
automatic segmentation of cell nuclei in fluorescence
microscopy images by more involved methods.
6 CONCLUSIONS
In this paper we addressed the benchmarking of cell
nuclei segmentation algorithms with a particular fo-
cus on fluorescence microscopy images. Specifi-
cally, we first described the considered dataset and ex-
plained how this data needs to be processed to serve
our purposes, where we in particular pointed to sev-
eral snares that might distort results if not properly
taken care of. Afterwards, we recalled the water-
shed transformation and gave a detailed account about
our implementation of a segmentation pipeline built
around this well-known image decomposition tool.
Next, we provided a review of three classes of well-
established evaluation measures for image segmenta-
tion. Finally, we briefly compared the performance
of several variants of our watershed segmentation
pipeline, where we not only relied on the previously
discussed quantitative evaluation measures but rather
combined it with a visual inspection of the cell bound-
ary images to collate the obtained results with our ex-
pectations. Everything combined, we thus explained
the set-up of a watershed segmentation pipeline that
might serve well as a baseline for the assessment of
more sophisticated cell nuclei detection methods and
as such constitutes an important component for our
future efforts to contribute to a more personalised im-
mune checkpoint inhibitor-based cancer therapy.
ACKNOWLEDGEMENTS
This work has been supported by the European Union
and the federal state of North-Rhine-Westphalia
(EFRE-0801303).
The authors would like to thank L. P. Coelho for
sharing useful additional information concerning the
publicly available data and algorithms.
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