# Improving cell instance segmentation in scanning electron microscopy via semantic image synthesis

Florian Robert, Alexia Calovoulos, Laurent Facq, Fanny Decoeur, Etienne Gontier, Christophe F Grosset, Baudouin Denis de Senneville

## ► To cite this version:

Florian Robert, Alexia Calovoulos, Laurent Facq, Fanny Decoeur, Etienne Gontier, et al. Improving cell instance segmentation in scanning electron microscopy via semantic image synthesis. ISBI 2025 - International Symposium on Biomedical Imaging, IEEE, Apr 2025, Houston, United States. hal-05036035

HAL Id: hal-05036035

https://hal.science/hal-05036035v1

Submitted on 16 Apr 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_1.png" /></figure>
IMPROVING CELL INSTANCE SEGMENTATION IN SCANNING ELECTRON
MICROSCOPY VIA SEMANTIC IMAGE SYNTHESIS

Florian Robert¹,²,³, Alexia Calovoulos³,⁴, Laurent Facq¹, Fanny Decoeur⁴,
Etienne Gontier⁴, Christophe F. Grosset³ and Baudouin Denis de Senneville¹,²

¹ Univ. of Bordeaux, CNRS, Institut de Mathématiques de Bordeaux, IMB, UMR5251,
351 cours de la Libération, Talence, F-33400, France

² INRIA Bordeaux, MONC team, 200 avenue de la Vieille Tour, Talence, F-33400, France

³ Univ. of Bordeaux, INSERM, Bordeaux Institute in Oncology, BRIC, U1312, MIRCADE team,
146 rue Léo Saignat, Bordeaux, 33000, France

⁴ Univ. of Bordeaux, CNRS, INSERM, Bordeaux Imaging Centre, BIC, UAR 3420, US 4,
146 rue Léo Saignat, Bordeaux, 33000, France

ABSTRACT

Understanding tumour tissue architecture is essential for advancing
new therapies and necessitates accurate cell instance segmentation in
serial scanning electron microscopy (SEM) images. While artificial
intelligence (AI)-based methods are effective for this task, errors per-
sist in poor-quality cell contours, requiring manual gap filling.

This study addresses cell labelling and contour segmentation by
supplementing a standard segmentation technique (nnU-Net) by a
specialized cell contour filling network. To train the latter, our ap-
proach utilizes a generative adversarial network (GAN)-based im-
age synthesis method to generate realistic disturbed contour maps
from randomized coarse cell contour semantic maps (CCSM). Sub-
sequently, cell labelling was produced using the enhanced contours.

Our approach was evaluated on private serial block face-
SEM (SBF-SEM) images of hepatoblastoma tissues, achieving accu-
rate cell labelling (70.8% labelling) and cell delineations (centerline
Dice (clDice)= 0.91). This outperforms the state-of-the-art Cell-
pose algorithm (29.2% labelling, clDice= 0.34), opening the path
to bioarchitecture investigations.

***Index Terms***— Scanning electron microscopy, Segmentation, Cell, Deep neural network, Generative adversarial network

1. INTRODUCTION

In recent years, volume electron microscopy (VEM) has become a
crucial imaging modality for studying entire biological tissue ultra-
structure [1, 2]. This technology enables the 3D visualization of
organelles such as nuclei, mitochondria, nucleoli, and lipid droplets
and so on. In the field of oncology, the potential for breakthroughs
in cancer therapy lies in a deeper understanding into the intracellular
and intercellular ultrastructure, as well as deciphering the mecha-
nisms governing tumour tissue formation and development [3, 4].
Exploring bioarchitectural parameters within and among tumour
cells shows significant potential [5].

These advancements require the segmentation of biological el-
ements of interest in hundreds and thousands of serial high-quality
images from large volume of tumour tissues, a task for which man-
ual and semi-automatic methods are unsustainable. Recently, arti-
ficial intelligence (AI) has addressed this challenge by introducing
automatic image segmentation methods [6, 7, 8, 9]. Particularly, in

[10, 11], the authors presented a self-configuring method for various
biological and medical image segmentation tasks, named nnU-Net,
which surpasses most existing approaches. Cell labelling and cell
contour segmentation, however, present a particularly challenging
task due to the uncontrolled proliferation of cells in tumour, lead-
ing to increased pressure and densely packed cells. Additionally, the
visibility of cell membranes varies depending on the tissue sample
preparation method and imaging modality parameters. Hence, exist-
ing approaches for cell or contour segmentation may still in practice
rely on laborious manual corrections of residual errors, posing a dis-
tinctive and notable challenge addressed in this study.

In this context, residual errors may be located in areas where
the cell contours are not straightforward, even for a biologic expert.
These issues involves filling gaps in cell contour segmentation and
may be addressed with image inpainting techniques. Currently, the
most efficient methods utilize convolutional neural network (CNN)
models that can independently identify missing line segments and re-
store them with correct thickness and curvature, even in images with
multiple gaps [12, 13]. However, obtaining realistic training data,
specifically images as closely resembling those in the case study, is
an important limitation tackled in the current study.

In this study, the standard nnU-Net segmentation algorithm is
supplemented with a novel AI-based cell contour filling network de-
signed to detect and fill in missing information areas. The training
of this network relies on cell contour probability maps with realistic
local distortions. To generate these maps, we employed a genera-
tive adversarial network (GAN)-based conditional image synthesis
method [14], which uses innovative randomized and coarse cell con-
tour semantic map (CCSM) as input. We assessed the effectiveness
of our approach for cell labelling (assigning a unique label to each
cell) and cell contour segmentation (reducing the need for residual
manual correction) using private high-quality scanning electron mi-
croscopy (SEM) images from patient-derived xenograft (PDX) hep-
atoblastoma tumour tissue.

## 2. MATERIAL AND METHODS

**2.1. Proposed cell instance segmentation pipeline**

The proposed method for segmenting and individualising cells in
SEM images involves three trained networks: one dedicated to ini-
<figure><img src="image_2.png" /><figcaption>**Fig. 1:** (a): Proposed pipeline for segmenting and labelling individually cells in serial SEM images, (b): Overview of the three networks employed in the segmentation pipeline. Blue rectangles denote networks intended for training. Green diamonds represent trained networks utilized for inference purposes.</figcaption></figure>

tial cell contour segmentation and another for cell contour filling. Additionally, a third network (GAN) is introduced to produce significant training data for the cell contour filling network. Figure 1a provides an overview of our cell instance segmentation pipeline.

### 2.1.1. Initial cell contour segmentation network (nnU-Net)

To generate cell contour probability maps from SEM images, the nnU-Net (3D architecture)<fnref n="1" /> [10, 11] was employed. It is worth noting that the original nnU-Net implementation allowed for saving probability maps by skipping the last activation layer.

### 2.1.2. Proposed cell contour filling network

At this stage, cell contour correction was addressed through gap detection and filling using a dedicated network based here again on the nnU-Net algorithm [10, 11]. It is important to note that a 2D architecture was employed to allow direct comparison with competing cell instance segmentation algorithms, which operate in 2D (see section 2.2.5). The proposed contour filling network was trained to predict accurate segmentation (ground truth) from cell contour probability maps with missing or poor informations (see Fig. 1b).

Once trained, the cell contour filling network was applied iteratively until the change in pixel values between two consecutive iterations fell below a threshold of 0.5% (see Fig. 1a).

### 2.1.3. GAN-based realistic probability map synthesis

To train the proposed cell contour filling network, a GAN-based conditional image synthesis method, specifically a SPatially-Adaptative (DE) normalization (SPADE) GAN<fnref n="2" /> [14], was employed to generate realistic cell contour probability maps from coarse and randomized CCSM as input (see Fig. 1b).

<figure><img src="image_3.png" /><figcaption>**Fig. 2:** (a): Private SEM image, (b): Typical example of CCSM generated from ground truth cell contour segmentation of (a) through Eq. (2) (background=black pixels/label 0; cell contours=white pixels/label 1; cell contour areas randomly assigned to the generation of local signal drops=red pixels/label 2), (c): Realistic cell contour probability map generated by the GAN using (b) as input. Realistic local signal drops in the cell contour, as seen in (c), correspond to the pixels highlighted in red within the semantic map displayed in (b).</figcaption></figure>

Real cell contour probability maps $\tilde{u}$ were obtained using the initial contour segmentation, while CCSM were derived as follows:

$$CCSM(u, \tilde{u})(\mathbf{x}) = u(\mathbf{x}) (1 + \mathbb{1}_{\tilde{u}(\mathbf{x}) \ge 0.5}(\mathbf{x})) \quad (1)$$

$\mathbf{x} \in \Omega$ denotes the pixel coordinates in the image domain $\Omega \subset \mathbb{R}^2$ and $u$ the ground truth cell contour segmentation.

### 2.1.4. Cell contour semantic map (CCSM) synthesis

Additional CCSM are required to generate a large set of cell contour probability maps with realistic local distortions (see Fig. 2), which are crucial for training the contour filling network (see Fig. 1b). These CCSM were randomly simulated from ground truth cell contour segmentation $u$:

$$CCSM(u)(\mathbf{x}) = u(\mathbf{x}) \left( 1 + \sum_{i=1}^{N} \mathbb{1}_{\|\mathbf{x}-\mathbf{x}_i\|_1 \le R_i}(\mathbf{x}) \right) \quad (2)$$

$\mathbf{x} \in \Omega$ denotes the pixel coordinates in the image domain $\Omega \subset \mathbb{R}^2$ and $N$ the number of locally disturbed areas. The disturbed areas consist of distinct square patches with side $R_i$, centred on randomly positioned cell contour pixels with coordinates $\mathbf{x}_i$. The lengths $R_i$ of the sides were drawn randomly following a uniform distribution $U([R_{min}, R_{max}])$. In the resulting CCSM, background pixels were labelled as 0, contour pixels as 1 and contour areas randomly assigned to the generation of realistic local signal drops were labelled as 2 (see Fig. 2b). The above method involves three hyper-parameters, the setup of which is outlined in section 2.2.3.

### 2.1.5. Individual cell labelling

At this point, we have cell contours to correctly identify individual cells. In this way, a connected component labelling was performed (see Fig. 1a), using the SimpleITK v2.2.1 and Scikit-image v0.22.0 libraries.

<footnote n="1">¹Available at: https://github.com/MIC-DKFZ/nnUNet/tree/v2.3.1</footnote>

<footnote n="2">²Available at: https://github.com/NVlabs/SPADE</footnote>
## 2.2. Experimental setup

### 2.2.1. Datasets

This study was conducted on private image stacks acquired using serial block face-SEM (SBF-SEM) [15] from two distinct PDX hepatoblastoma tissues [5]. The training set was derived from the first tissue sample, while two testing sets were used: one intra-tissue (same tumour but different location) and one extra-tissue (different tumour).

The training set for the initial nnU-Net-based cell contour segmentation network (section 2.1.1) comprises 19, 571 cell delineations within 246 slices acquired with the following acquisition parameters: voxel size = 0.015 × 0.015 × 0.1 µm<fnref n="3" /> and Field Of View (FOV) = 75 × 75 µm². Cell contour were manually delineated using a semi-automatic method, as elaborated in [5], with each delineation producing a unique mask for every individual cell. From these, 20 × 246 realistic cell contour probability maps were randomly synthesised using Eq. (2) and the GAN to train the proposed cell contour filling network (section 2.1.2). Additionally, 630 images were acquired using the following parameters: voxel size = 0.015 × 0.015 × 0.05 µm<fnref n="3" /> and FOV = 90 × 90 µm². Cell contours were manually delineated on every ten slices, yielding 6, 867 delineated cells across 63 slices for filling network fine-tuning (section 2.1.2). Additionally, the GAN network was trained on these 63 slices for 200 epochs with a batch size of 8 and a learning rate that decayed from epoch 100 to 200. The same acquisition parameters were used for 300 intra-tissue and 1, 050 extra-tissue testing slices, with only 30 and 21 slices manually delineated, respectively. This yielded 3, 425 intra-tissue and 1, 282 extra-tissue cell delineations.

### 2.2.2. Image preprocessing

To standardize intensity, z-scoring was applied to each slice individually. All images were then resized to ensure a consistent in-plane resolution of 512 × 512 pixels. Alignment within each stack was performed to correct for spatial drifts, specifically translations, that occurred during SBF-SEM acquisition. This was achieved using the StackReg module from the `pystackreg v0.2.7` library.

### 2.2.3. Hyper-parameters setup

It should be emphasized that Eq. (2) involves three hyper-parameters, which were set to standard values to ensure realistic GAN-synthesised probability maps: $N = 15$, $R_{min} = 2$µm, and $R_{max} = 14$µm.

### 2.2.4. Approach assessment

The efficiency of our proposed pipeline for individual cell labelling and enhanced cell contour segmentation was evaluated using the dedicating metrics:

**Labelling metrics:** We computed the percentage of accurately individually labelled cells. A cell was considered as correctly segmented if the overlap between its mask and the corresponding mask in the ground truth exceeded 85%. To provide additional insights into the nature of required corrections, the percentages of merged cells (false negative contours) and split cells (false positive) were calculated.

**Segmentation metrics:** We used the generic framework for metric selection, proposed in [16], to compute the Normalized Surface Distance (NSD) and centerline Dice (clDice).

**Statistical analysis:** An asymmetric Mann-Whitney U Test (the SciPy `v1.12.0` library was used) was performed on these metrics. Results were deemed statistically significant when the *p*-value fell below the conventional threshold of 0.05.

<figure><img src="image_4.png" /><figcaption>**Fig. 3:** Visual comparison of results obtained on the extra-tissue testing dataset. (a) : private SEM image, (b-e) : white colour represents cell contour, red colour represents blood capillary (BC) and haemorrhagic zone (HZ) obtained through manual segmentation, coloured cells represents accurately labelled cells compared to ground truth (b), and black indicates errors as compared to ground truth.</figcaption></figure>

### 2.2.5. Comparison with a competing cell instance segmentation algorithm (Cellpose)

We compared our pipeline with Cellpose (v3.0.8)<fnref n="3" />, a state-of-the-art 2D generalist algorithm for cellular segmentation across various image types, which has been continually enhanced and supported to date [8, 9, 17]. To ensure a fair comparison with our approach, the latest publicly available "cyto3" model was fine-tuned using our private training datasets (which are described in section 2.2.1).

### 2.2.6. Hardware details

All training and model evaluations have been completed on a computer running Ubuntu 20.04.6 LTS with the Intel(R) Xeon(R) Gold 6248R CPU and a NVIDIA RTX A6000 GPU with 48 GB of VRAM. All implementation were performed using Python 3.9.

## 3. RESULTS

Figure 3 provides a visual comparison of the results obtained using the proposed approach and the competing methods on the extra-tissue testing dataset (1, 282 cell delineations). The input SEM image (Fig. 3a) and the manual ground truth segmentation (Fig. 3b)

<footnote n="3">³Available at: https://github.com/MouseLand/cellpose/tree/v3.0.8</footnote>
<table><caption>**Table 1:** Assessment of the proposed pipeline (Fig. 1a) in private high-quality SEM tumour tissue images (intra-tissue testing dataset = 3, 425 cell delineations, extra-tissue = 1, 282 cell delineations). Best results are indicated in bold font, and * denotes the outcomes of our proposed pipeline that exhibit significant improvement compared to the initial cell contour segmentation network alone (p < 0.05).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Labelling metrics</th><th colspan="2">Segmentation metrics</th></tr><tr><th>Labelled cells<br>[%]</th><th>Merged cells<br>[%]</th><th>Split cells<br>[%]</th><th>NSD<br>[A.U.]</th><th>clDice<br>[A.U.]</th></tr></thead><tbody><tr><td><strong>Cellpose [8]</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intra-tissue testing dataset</td><td>42.8 ± 4.4</td><td>20.5 ± 4</td><td>36.8 ± 4.4</td><td>0.81 ± .01</td><td>0.37 ± .03</td></tr><tr><td>Extra-tissue testing dataset</td><td>29.2 ± 6.1</td><td>31 ± 6.6</td><td>39.7 ± 9.3</td><td>0.66 ± .05</td><td>0.34 ± .04</td></tr><tr><td><strong>nnU-Net</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intra-tissue testing dataset</td><td>61.6 ± 4.1</td><td>21.7 ± 4.4</td><td><strong>16.7 ± 3.3</strong></td><td>0.94 ± .01</td><td>0.89 ± .01</td></tr><tr><td>Extra-tissue testing dataset</td><td>24 ± 9.1</td><td>73.5 ± 9.5</td><td><strong>2.5 ± 2.5</strong></td><td>0.81 ± .05</td><td>0.80 ± .04</td></tr><tr><td><strong>nnU-Net + Contour filling</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intra-tissue testing dataset</td><td><strong>71.4* ± 4</strong></td><td><strong>8.5* ± 2.6</strong></td><td>20.1 ± 3.6</td><td><strong>0.96 ± .01</strong></td><td><strong>0.91 ± .01</strong></td></tr><tr><td>Extra-tissue testing dataset</td><td><strong>70.8* ± 9.4</strong></td><td><strong>19.9* ± 7.9</strong></td><td>9.3 ± 4.8</td><td><strong>0.93* ± .02</strong></td><td><strong>0.91* ± .03</strong></td></tr></tbody></table>

are reported, along with results obtained using the Cellpose algorithm [8] (Fig. 3c), the initial nnU-Net-based cell contour segmentation (Fig. 3d) and the proposed additional cell contour filling network (Fig. 3e). While a comparable number of correctly labelled cells was observed using Cellpose and nnU-Net, the number of accurately labelled cells significantly increased using our proposed approach (Fig. 3e). In addition, cell contours were more precisely delineated when segmented using methods specifically designed to this structure (see Fig. 3c-d).

Table 1 confirms these visual observations for both intra-tissue and extra-tissue testing datasets. The cell contour filling network, trained using the proposed GAN-based realistic probability map synthesis, notably improved the segmentation metrics (ie. NSD and clDice) compared to the nnU-Net and the Cellpose. Furthermore, the iterative inference process employed by the contour filling network led to significant improvements in the proportion of correctly labelled cells. On the extra-tissue testing dataset, the proportion of accurately labelled cells increased markedly from 24 ± 9.1% with the nnU-Net to 59.4 ± 12.6% after the first iteration of the filling network, and further to 70.8 ± 9.4% upon full convergence after the seventh iteration. On the intra-tissue testing dataset, convergence was achieved at the 15th iteration, resulting in 71.4% of correctly labelled cells. These performances were achieved with only a minor increase in the proportion of erroneously split cells. These findings highlight the effectiveness of the proposed pipeline in significantly enhancing standard cell contour segmentation (nnU-Net) and surpassing the performance of the state-of-the-art cell instance segmentation method (Cellpose).

## 4. DISCUSSION

Our study introduces an innovative cell contour filling network tailored to improve cell contour segmentation and cell labelling in serial SEM images. The first network of the proposed pipeline, based on the nnU-Net framework, enables the calculation of cell contour probability maps containing valuable information that can be utilized by an additional contour filling network. The latter was trained using an innovative data synthesis strategy to produce realistic probability maps with poor or missing information. This data synthesis strategy relies on simulated CCSM and a GAN-based network.

Our study highlights the potential of the proposed approach

for precise gap detection and filling, significantly improving cell labelling while minimizing manual intervention. As demonstrated in Table 1, the cell contour filling network plays a crucial role in reducing the proportion of erroneously merged cells, leading to a notable decrease in false negatives in the corrected cell contours.

Cellpose [8] was used as a baseline to evaluate the performance of our proposed pipeline. It is important to note that the Cellpose algorithm utilizes a 2D CNN architecture along with a gradient ascent procedure for labelling, which introduces constraints on cell size variability. This posed challenges when applying the method to our PDX datasets, as the 2D slices extracted from 3D datasets exhibit a wide range of cell sizes in the resulting in-plane images.

One of the main limitations of our study is the utilization of a 2D architecture for both the cell contour filling network and the GAN-based data synthesis network, primarily due to challenges in acquiring manually segmented data. Although there is a reasonable expectation that performance could be enhanced by employing 3D architecture, adopting such an approach introduces additional challenges such as data acquisition, computational memory costs, and processing time.

## 5. CONCLUSION

The proposed approach notably diminishes the efforts required for manual cell contour delineation in SEM images. This was achieved by integrating two trained networks into a robust cell instance segmentation pipeline: a standard cell contour segmentation algorithm and a cell contour filling network. To address the issue of limited training data for the filling network, we introduced an innovative simulation strategy based on CCSM and a GAN-based conditional image synthesis method. Future works will focus on extending the proposed cell contour filling network to the 3D network architecture to further enhance its performance.

## 6. ACKNOWLEDGMENTS

Computational analysis was carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régional d'Aquitaine (see https://www.plafrim.fr/). Imaging was performed
on the Bordeaux Imaging Center, member of the FranceBioImaging
national infrastructure (ANR-10-INBS-04).

This work was supported by grants to FR from Inserm and Inria in the frame of the "Santé Numerique" joint program and the Institut National du Cancer (INCa) (N° 2023-018). CFG and MIRCADE also received donations from the following charities: Aidons Marna; Aline en Lutte contre la Leucémie; Cassandra; E.S.C.A.P.E.; Eva pour la Vie; Fondation for Addie's Research; Grandir sans Cancer; Les Amis de Marius; Les Récoltes de l'Espoir; Monaco Liver Disorder, Nathanaël, du Rêve et de l'Espoir; Noëline, ma Fille, ma Bataille; Pour Emma; Scott & Co; Tous Avec Agosti/Kiwanis International and Warrior Enguerrand. The authors don't have any financial benefit from those Foundations and charities. Dr C.F. Grosset received a 2-year research grant from Aquitaine Science Transfer and is part of the scientific board of Paediatis.

7. REFERENCES

[1] Lucy M. Collinson, Carles Bosch, Anwen Bullen, Jemima J. Burden, Raffaella Carzaniga, Cheng Cheng, Michele C. Darrow, Georgina Fletcher, Errin Johnson, Kedar Narayan, Christopher J. Peddie, Martyn Winn, Charles Wood, Ardan Patwardhan, Gerard J. Kleywegt, and Paul Verkade, "Volume em: a quiet revolution takes shape." *Nature Methods*, vol. 20, no. 6, pp. 777-782, 2023.

[2] Jessica L. Riesterer, Claudia S. López, Erin S. Stempinski, Melissa Williams, Kevin Loftis, Kevin Stoltz, Guillaume Thibault, Christian Lanicault, Todd Williams, and Joe W. Gray, "A workflow for visualizing human cancer biopsies using large-format electron microscopy," *bioRxiv*, 2019.

[3] Mingqi Han, Eric A. Bushong, Mayuko Segawa, Alexandre Tiard, Alex Wong, Morgan R. Brady, Milica Momcilovic, Dane M. Wolf, Ralph Zhang, Anton Petcherski, Matthew Madany, Shili Xu, Jason T. Lee, Masha V. Poyurovsky, Kellen Olszewski, Travis Holloway, Adrian Gomez, Maie St. John, Steven M. Dubinett, Carla M. Koehler, Ori an S. Shirihai, Linsky Stiles, Aaron Lisberg, Stefano Soatto, Saman Sadeghi, Mark H. Ellisman, and David B. Shackelford, "Spatial mapping of mitochondrial networks and bioenergetics in lung cancer," *Nature*, vol. 615, no. 7953, pp. 712-719, 03 2023.

[4] Archana Machireddy, Guillaume Thibault, Kevin G. Loftis, Kevin Stoltz, Cecilia E. Bueno, Hannah R. Smith, Jessica L. Riesterer, Joe W. Gray, and Xubo Song, "Segmentation of cellular ultrastructures on sparsely labeled 3d electron microscopy images using deep learning," *Frontiers in Bioinformatics*, vol. 3, pp. 1308708, 2023.

[5] Baudouin Senneville, Fatma Zohra Khoubai, Marc Bevilacqua, Alexandre Labedade, Kathleen Flosseau, Christophe Chardot, Sophie Branchereau, Jean Ripoche, Stefano Cairo, Etienne Gontier, and Christophe Grosset, "Deciphering tumour tissue organization by 3d electron microscopy and machine learning," *Communications Biology*, vol. 4, 12 2021.

[6] Fidel A. Guerrero-Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, and Alexandre Cunha, "Multiclass weighted loss for instance segmentation of cluttered cells," in *2018 25th IEEE International Conference on Image Processing (ICIP)*, 2018, pp. 2451–2455.

[7] Olaf Ronneberger, Philipp Fischer, and Thomas Brox, "U-net: Convolutional networks for biomedical image segmentation,"

in *Medical Image Computing and Computer-Assisted Intervention: MICCAI 2015*, Nassir Navab, Joachim Hornegger, William Wells, and Alejandro Frangi, Eds., Cham, 2015, vol. 9351 of *Lecture Notes in Computer Science*, Springer.

[8] Carsen Stringer, Tim Wang, Michalis Michaelos, and Marius Pachitariu, "Cellpose: a generalist algorithm for cellular segmentation," *Nature Methods*, vol. 18, no. 1, pp. 100–106, 01 2021.

[9] Marius Pachitariu and Carsen Stringer, "Cellpose 2.0: how to train your own model," *Nature Methods*, vol. 19, no. 12, pp. 1634–1641, 2022.

[10] Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein, "nnu-net: a self-configuring method for deep learning-based biomedical image segmentation," *Nature Methods*, vol. 18, no. 2, pp. 203–211, 02 2021.

[11] Fabian Isensee, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus Maier-Hein, and Paul F. Jaeger, "nnu-net revisited: A call for rigorous validation in 3d medical image segmentation," 2024.

[12] Kazuma Sasaki, Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa, "Joint gap detection and inpainting of line drawings," in *2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017, pp. 5768–5776.

[13] Hao Chen, Mario Valerio Giuffrida, Peter Doerner, and Sotirios A. Tsaftaris, "Root gap correction with a deep inpainting model," in *British Machine Vision Conference*, 2018.

[14] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu, "Semantic image synthesis with spatially-adaptive normalization," in *2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*, 2019, pp. 2332–2341.

[15] Winfried Denk and Heinz Horstmann, "Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure," *PLOS Biology*, vol. 2, no. 11, 10 2004.

[16] Lena Maier-Hein, Annika Reinke, Peter Godau, Mohammad D Tizabi, Felix Buettner, Evangelos Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Martin A Riegler, Manuel Wiesenfarth, Anil E Kavur, Carole H Sudre, Michael Baumgartner, Matthias Eisenmann, Daniel Heckmann-Nötzel, Thomas Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Sotirios Bakas, Aristeidis Benis, Matthew B Blaschko, M Jorge Cardoso, Veronika Cheplygina, Beth A Cimini, Gary S Collins, Keyvan Farahani, Lluís Ferrer, Adrian Galdran, Bram van Ginneken, Rolf Haase, Daisuke A Hashimoto, Matthew M Hoffman, Menno Huisman, Pierre Jannin, Charles E Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Felix Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A Landman, Geert Litjens, Ali Madani, Klaus Maier-Hein, Anne L Martel, Per Mattson, Erik Meijering, Bjoern Menze, Karel G M Moons, Henning Müller, Boris Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I Sánchez, Sharmila Shetty, Maarten van Smeden, Ronald M Summers, Abdel Aziz Taha, Alexander Tiulpin, Sotirios A Tsaftaris, Ben Van Calster, Gaël Varoquaux, and Paul F Jäger, "Metrics reloaded: Recommendations for image analysis validation," *Nat Methods*, vol. 21, no. 2, pp. 195–212, Feb 2024, Epub 2024 Feb 12.

[17] Carsen Stringer and Marius Pachitariu, "Cellpose3: one-click image restoration for improved cellular segmentation," *bioRxiv*, 2024.