Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson’s Disease
Authors: Fatemeh Haghighi
soumitra ghosh
Sarah Chu
Hai Ngu
Mohsen Hejrati
Han Hui Lin
Baris Bingol
Somaye Hashemifar
Venue: Accepted by TMLR ()

Abstract: Parkinson’s Disease (PD) is the second most common neurodegenerative disease in humans. PD is characterized by the gradual loss of dopaminergic neurons in the Substantia Nigra (SN, a part of the mid-brain). Counting the number of dopaminergic neurons in the SN is one of the most important indexes in evaluating drug efficacy in PD animal models. Currently, analyzing and quantifying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images which is laborious, time-consuming, and highly subjective. As such, a reliable and unbiased automated system is demanded for the quantification of dopaminergic neurons in digital pathology images. Recent years have seen a surge in adopting deep learning solutions in medical image processing. However, developing high-performing deep learning models hinges on the availability of large-scale, high-quality annotated data, which can be expensive to acquire, especially in applications like digital pathology image analysis. To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models. To the best of our knowledge, this is the first deep learning model that detects the cell body of dopaminergic neurons, counts the number of dopaminergic neurons, and provides characteristics of individual dopaminergic neurons as a numerical output. Extensive experiments demonstrate the effectiveness of our model in quantifying neurons with high precision, which can provide a faster turnaround for drug efficacy studies,better understanding of dopaminergic neuronal health status, and unbiased results in PD pre-clinical research. As part of our contributions, we also provide the first publicly available dataset of histology digital images along with expert annotations for the segmentation of TH-positive DA neuronal soma.

Published in Transactions on Machine Learning Research (10/2023)
Self- supe-vupd Learning for Segmentation and Quantifica- tion of Dopamine Neurons in Parkinson's Disease
Fatemeh Haghighi
fhaghigh@asu.edi
Arizona State University, Tempe, AZ 85281, USA Department of Neuroscience, Genentech Inc., South Son Prancisco, CA 94080, USA
Soumitra Ghosh'
ghoshs29@geeee.com
Department of Neuroscience, Genentech Inc., South Son Francisco, CA 94080, USA
Hai Ngu
hain&ggeee.c com
Department of Pathology, Genentech Inc., South San Francisco, CA 94080, USA
Sarah Chu
chuss 1 18@@geg@@.nnc.. com
Department Neuroscience, Genentech Inc., South Son Francisco, CA 94080, USA
Han Lin
hanhlin@gene.com
Department of Neuroscience, Genentech Inc., South Son Francisco, CA 94080, USA
Mohsen Hejrati
hejratis@@eeee.com
Depariment of Artificial Intelligence, Genentech Inc., South San Prancisco, CA 94080, USA
Baris Bingol
barish@gene.comm
Department of Neuroscience, Genentech Inc., South San Francisco, CA 94080, USA
Somaye Hashemifar"
hashems, Beres44@@eee.ee com
Department of Artificial Intelligence, Genentech Inc., South San Francisco, CA 94080, USA
Reviewed on OpenReview: https: // openreview. net/ met/ forum? id= izFnURFG3f
Abstract
Parkinson's 's Discase (PD) is the sccond most common neurodegenerative discase in humans. PD characterized by the gradual loss of dopaminergic neurons in the Substantia Nigra (SN, part of the mid-brain). Counting the number of dopaminergic neurons in the SN is one of the most important indexes in evaluating drug efficacy in PD animal models. Cur- rently, analyzing and quantilying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images which is laborious, time-consuming, and highly subjective. As such, a reliable and unbiased automated system is demanded for the quan- tification of dopaminergic neurons in digital pathology images. Recent years have seen surge in adopting deep learning solutions in medical image processing. However, developing high-performing deep learning models hinges the availability of large-scale, high-quality annotated data, which can be expensive to acquire, especially in applications like digital pathology image analysis. To this end, we propose an aad-to-end deep learning framework based on self-supervised lcarning for the segmentation and quantification of dopaminergic neurons in PD animal models. To the best of our knowledge, this the first deep learning model that detects the cell body of dopaminergic neurons, counts the number of dopaminer- gic neurons, and provides characteristics of individual dopaminergic neurons a numerical output. Extensive experiments demonstrate the effectiveness of our model in quantifying neurons with high precision, which can provide faster turnaround for drug elficacy sludies,
"Corresponding authors

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Published in Transactions on Machine Learning Research (10/2023)
better understanding of dopaminergic neuronal health status, and unbiased results in PD pre-clinical research. As part of our contributions, we also provide the first publicly avail- able dataset of histology digital images along with expert annotations for the segmentation of TH-positive DA ncuronal soma.
1 Introduction
Parkinson's Disease (PD) is the second most common neurodegenerative disease in humans (Poewe et al., 2017). PD is characterized by the gradual loss of dopaminergic neurons (DA neurons) in the Substantia, Nigra (SN). SN is the area of the midbrain that consists of DA neurons which are most susceptible to genetic and sporadic factors and lost in PD. Toss of dopaminergic neurons leads to motor neuron associated dysfunctions as observed in PD patients and animal models (Johnson et al., 2012). Preventing loss of DA neurons is the most important goal of PD targeting therapics. Immunostaining of Tyrosine Hydroxylase (TH), an enzyme expressed predominantly
DA neurons, is the most. reliable method used for detecting DA neurons, and the TH staining intensity is also an indicator of the health status of the DA ncurons (Ghosh et al., 2021). Given these factors, pre-clinical research on PD is highly dependent on the segmentation and quantification of DA neurons in the SN (Poewe et al., 2017; Guatteo et al., 2022). Currently, analyzing and quantifying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images, which is laborious, time-consuming, and highly subjective. As such, reliable and unbiased automated systems are highly demanded for the quantification of dopaminergic neurons in digital pathology images. Such systems will make a significant impact in the field of PD pre-clinical research by identifying the efficacy of potent drugs in a shorter timo-framo and accelorating the possibility of taking a potential drug into the clinic.
Recent years have seen surge in adopting deep learning solutions in various medical image processing appli- cations, including cell segmentation in microscopy images. Numerous studies have developed deep learning- based cell segmentation methods that are specialized for images with large-scale training datasets (Falk et al., 2019). However, the scarcity and difficulty in accessing labeled data for DA neurons pose a significant challenge to the success of these methods for segmentation and quantification of DA neurons. Morcover, due to the unique morphology and organization of DA neurons, the efficiency of generalist cell segmen- tation methods (Stringer et al., 2021) for segmentation of DA neurons may be limited (Robitaille et al., 2022). Furthermore, generalist cell segmentation methods fall short in providing specific information about the characteristic of DA neurons, which holds substantial value in PD rescarch. Therefore, it is crucial to develop specialized machine learning models that can analyze and quantify DA neurons precisely in the SN.
To address these challenges, we propose an end-to-end deep learning framework for the segmentation and quantification of dopaminergic neurons in the SN of mouse brain tissues. To the best of our knowledge, this is the first deep learning model that segments the cell body of dopaminergic neurons, counts the number of dopaminergic neurons, and provides characteristics of individual dopaminergic neurons as a numerical output. Particularly, to overcome the labeled data scarcity challenge, our proposed method leverages cross-domain self supervised learning (Zbontar et al., 2021; Azizi et al., 2021; Caron et al., 2021; Haghighi et al., 2021; 2022) on both large-scale unlabeled natural images and domain specific pathology images. The self-supervised pre- trained models are further fine-tuned for the DA neuron segmentation using limited labeled data. Moreover, we propose a practical approach for counting the number of DA neurons from the segmented cells. Our extensive experiments demonstrate that our cross-domain self-supervised learning provides superior segmen- tation accuracy compared to (1) training segmentation models from scratch (random initialization), (2) fie-tuning conventional supervised ImageNet models, and (3) fine-tuning sell-supervised pretrained models just natural or in-domain datasets. Additionally, our experiments highlight the efficacy of our method in counting the number of DA neurons, which can provide faster turnaround for drug efficacy studics, better understanding of dopaminergic neuronal health status, and unbiased results in PD pre-clinical research.
We provide the first publicly available dataset of digital microscopy images along with expert annotations for the segmentation of TH-positive DA neurons mouse brains. We anticipate that this novel datasct will expedite the development and assessment of new machine learning models for the segmentation of DA neurons in digital pathology images, further advancing research in Parkinson's 's disease.
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In summary, we make the following contributions:
The first publicly available dataset of digital microscopy images along with expert annotations for the segmentation of TH-positive DA neurons.
The first end-to-end framework for automatic segmentation and quantification of DA neurons in whole-slide digital pathology images of PD models.
A cross-domain self-supervised pre-training approach that exploits the power of unlabeled natural and medical images for representation learning.
A comprchensive set of experiments that demonstrate the effectiveness of our method in segmenting and quantifying DA neurons using a limited amount of annotated data, and providing additional information about phenotypic characteristics of DA neurons.
2 Related Works
CNN-based quantification of dopaminergic neurons. The current strategies for analyzing DA neu- rons face significant challenges. Traditional stereology is extremely time consuming and subjective to user associated bias. Moreover, threshold for optical density based method cannot capture the complex nature of DA neurons on histology sections and provide inaccurate numbers. Recently, deep learning methods have been successfully utilized in analyzing human digital pathology images for different. lasks, including cell seg- mentation and cell counting Ronneberger et al. (2015); Falk et al. (2019); Greenwald et al. (2022); Moshkov et al. (2020); Hatipoglu & Bilgin (2017). Nucleus segmentation methods (Hollandi et al., 2022) detoct nuclci of cells, but they fail detect individual cells and cannot retrieve the information on cell body (i.e. TH intensity in soma) which can be used to interpret the biology of DA neurons. Generalist cell segmentation models such as Cellpose (Stringer et al., 2021) have been developed to segment many types of cell. Cellpose relies on a large dataset of images of different cells and a reversible transformation from the training set segmentation masks to vector gradients that can be predicted by a neural network. In particular, it leverages a U-Net model to predict the horizontal and vertical gradients, as well as whether a pixel belongs to any cell. The three predicted maps are then combined into gradient vector field. The predicted gradient vector fields are used to construct dynamical system with fixed points whose basins of attraction represent the predicted masks. Despite its success, the efficiency of Cellpose in detecting specific types of neurons such as DA neurons is still limited (Robitaille et al., 2022). The number of studies that employ deep learning for the quantization of DA neurons in animal models of PD is relatively limited. Penttinen et al (Penttinen et al., 2018) implemented a deep learning- based method for processing whole-slide digital imaging to count DA ncurons in SN of rat and mouse models. This study leverages the TH positive mucleus to detect the TH cells which susceptible to error because of the architecture of DA neurons in SN and makes it difficult distinguish between overlapping cells when detected only relying on the nucleus as annotations. Zhao et al (Zhao et al., 2018) developed framework for the automatic localization of SN region and detection of neurons within this region. The SN localization is achieved by using a Faster- RCNN network, whereas neuron detection is done using a LSTM network. However, these studies are limited to counting neurons and/or detecting neuron locations and do not provide additional information about individual cells, such as TH intensity and health status, which is essential for understanding the biology behind DA neuronal loss and its association with PD pathogenesis.
Self-supervised learning. Self-supervised learning methods aim to learn generalizable representations directly from unlabeled data. This paradigm involves training neural network on mamually created (pre- text) task for which ground truth is obtained from the data. The learned representations can be transferred and fine-tuned for various target tasks with limited labeled data. Instance discrimination methods (Zbontar et al., 2021; He et al., 2020; Caron et al., 2021; Grill el al., 2020; Chen & He, 2021; Chen el al., 2020) have recently sparked a renaissance in the SSL paradigm. These methods consider each image as a separate class and soek to learn representations that are invariant to image distortions. Contrastive approaches, such as MoCo (He et al., 2020), consider the augmented views of the same image as positive pairs and the ones from the other images negative pairs and aim to enhance the similarity between positive pairs while decreasing
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the similarity between negative pairs. Barlow Twins (Zbontar et al., 2021) minimizes redundancy in the representations by measuring the cross-correlation matrix between the embeddings of two views of the same image and making it close to the identity matrix. Clustering approaches, such as deep cluster (Caron et al., 2021) and SwAV (Caron et al., 2021), simultancously cluster the data while enforcing agreement between cluster assignments generated for different views of the samne image. Motivated by their success com- puter vision, instance discrimination SSL methods have been adopted in medical applications. A systematic transfer learning study for medical imaging (Hosseinzadeh Taher et al., 2021) demonstrated the efficacy of existing instance discrimination methods pre-trained on ImageNet for various medical tasks. A group of works focused on designing SSL frameworks by exploiting consistent anatomical structure within radiology scans (Taher et al., 2023; Haghighi et al., 2021; 2020). Another line of studies designed contrastive-basse SSL for medical lasks (Hosseinzadeh Taher et al., 2022; Haghighi et. al., 2022; Azizi et al., 2021; Kaku el al., 2021), including whole slide image classification (Li et al., 2021). In contrast to the previous works, our work is the first study that investigates the efficacy of SSL for digital pathology images of PD animal models compensate for the lack of large-scale annotated datasets for training deep learning models.
3 Dataset
The dopaminergic neuronal soma dataset used in this study is an internal dataset of Genentech and was obtained by manually labeling approximately 18,193 TH positive DA neuronal soma in 2D histology digital images. The digital images were obtained from different animal studies where mouse brains were sectioned at 35 micron thickness and stained with TH and either Haematoxylin or Nissl as a background tissue stain. Each animal study consisted of multiple animals and all the animals in one study were stained at one time- frame. This allowed us to take into account the minor tissue processing and staining associated variability that commonly occurs in histology studies. The sections were then imaged using a whole slide scanner microscope, Nanozoomer system (Hamamatsu Corp, San Jose, CA) at 20x resolution (0.46 microns microns/pixel). Whole coronal brain section images containing the SN were exported from the digital scans at 20x resolution and were used to annolate the TH positive DA neuronal soma and train the model. The ground truth (GT) for this study was labeled and quntity-c by biologists who specialize in mouse brain anatomy and PD rescarch. We have evaluated human annotator bias in one of our previous ncuroanatomy segmentation studies that have been published (Barzekar el al., 2023). We observed a 5-8 percent difference in oulput with different manual annotators. Since manual annotation and counting of individual cells is extremely laborious in dataset used in this study, to mitigate the human annotator bias, we had 3 annotators who randomly annotated the cells for the GT in this study. A neuroanatomy expert further QCd the annotations and made the necessary changes to reflect the cell annotation. The representative image is depicted in Fig 2 ground truth panel. The blind test dataset (out-study test set) used for analyzing the model's efficiency was a separate animal study in which the model has not been directly trained on the study group. This dataset consists of 20 brain sections randomly selected from a different animal study with 150 brain sections. The ground truth (GT) for the out-study test set was established by annotating the soma. Our model and the baseline method (i.e. Cellpose) were tested on these 20 sections. Our cell annotation dataset consists of 114 8-bit RGB images with associated 16-bit cell label images. For the label images, each cell area is assigned pixel value in the range of to n, where n is the total number of labeled cells in a label image. Since the label images are in 16-bit, they may appear black; pre-procossing (for example adjusting sachhtness/contraass is needed to see the cells. For all the images, the image resolution is 0.46 microns per pixel. For facilitating future research into PD, we have publicly released the dataset, which can be found at https://datal httpe:////aaa..mmmlleyy.y mendeley. /ttp///aaattt//p///aaa/a/e
4 Method
In this section, we provide the details of our deep learning framework, including the self-supervisod pre- training, fine-tuning stage, and cell counting.
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fø
fo
LISSL
Last
fo
90
(a) Self-supervised pre-training non-medical data
(b) Self-supervised pre-training medical
Supervised fine- tuninng on medical data
Figure An overview of our approach. To address the annotated data scarcity challenge for training deep models, we perform (a) self-supervised pre-training on natural images, and then (b) self-supervised pre- training on digital pathology images. We finally (c) fine-tune the self-supervised pre-trained model with limited annotated data for the target neuron segmentation task.
4.1 Self-supervised pre-training
Our approach is established continual self-supervised pre-training in which model is first pre-trained a massive general dataset, such as ImageNet, and then pre-trained on domain-specifie datasets. For the first step (sco Figure I.a), we leverage the self-supervised models pre-trained on the ImageNet datasct using state-of-the-arl instance discrimination approaches, such as Barlow Twins (Zbontar et al., 2021). For the second step (see Figure 1.b), we continue the sule-supe pre-training on the in-domain medical dataset. Finally, fine-tune the pro-trained models for the neuron segmentation (target) task using labeled images (see Figure 1.c).
Barlow Twins (Zbontar et al., 2021). This SSL approach aims to reduce the amount of redundant information about each sample in the learned representations while simultaneously making the representation invariant to image distortions. To do so, given an image sample X, two distorted views of the sample are generated by applying data augmentation function T(.) on X. The two distorted views X1 and X2 are then processed by the backbone network fe produce latent representations Zi (T((i))) and Z₂ fX(2)) The backbone network fa includes standard ResNet-50 encoder and three-layer MLP projection head. The model is trained by minimizing the following loss function:
LSSL = E - Ca)² + ΣΣ C
(1)
where C. is the cross-correlation matrix computed between Zi and Za along the batch dimension. À is coefficient to identify the weight of each loss term The model is trained by making the cross-correlation matrix C close to the identity matrix. In particular, by equating the diagonal clements of the C to 1, the learned representation will be invariant to the image distortions. By equating the off-diagonal elements of the C to O, the different elements of the representation will decorrelated, so that the output units contain non-redundant information about the images.
4.2 Network architecture
For self-supervised pre-training, we use a standard ResNet-50 as the backbone. For the target dopamine neuron segmentation task, we employ a U-Net network which consists of an encoder (Jo) and decoder (go). The encoder a standard ResNet-50, which initialized with the self-supervised pre-trained encoder. The decoder is initialized randomly.
4.3
Data sampling and augmentation
For the larget dopamine neuron segmentation lask, we divide images into non-overlapping patches of size 512x512 to ensure we sample from every part of the image. In all experiments, the raw image intensities per channel are normalized to the [0.1] range. Data augmentation is essential for biological and medical image analysis due to the typically limited amount of available annotated data. As such, we use different data augmentation techniques to enforce the model to capture robust and generalizable representations.

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Pre-training
Inirialization
(16
Pre-training
Initialization
Dice(())
Random
86,4310.99
Random
67.22:18.24
Supervised
ImageNet
35.86113337
Supervisod
ImageNet
76.76:14.25
DeepCluster-v2
87.1310.68
DeepChuster-v2
78.72:33.9
Self-supervised
Barlow Twins
87.24+0.75
Self-supervised
Barlow Twins
79.50:12.02
SwAV
87.73=10.68
SwAV
80.83:11.17
(a) Fine-tuning with 100% of data: the best self- supervised method (i.e SwAV) yields significant boost (p < 0.05) compared with the best baseline (i.e. training from random initialization).
(b) Fine-tuning with 25% of data: the best self- supervised method (i.e SwAV) yields significant boost (p < 0.05) compared with the best baseline (i.e. supervised ImageNet model).
Table 1: Comparison of different initialization methods on the dopamine neuron segmentation task.
In particular, based our ablation study, we Flip, Rotation, RGBShift, Blur, GaussianNoise,, and RandomResizedCrrpp to teach the expected appearance and color variation to the deep model.
4.4 tuning protocol
We initialize the encoder of the target model (i.e. U-Net) with the pre-trained models and fine-tune all target model parameters. We train the target models using the Adam optimizer with a lcarning rate of le - 3 and ($1, 32) = (0.9,0.999). We employ ReduceLRROnPlatenn learning rate decay schoduler. We use a batch size of 32 and train all models for 200 epochs. We employ the early-stop mechanism using the validation data avoid over-fitting. We use the Dice coefficient loss function for training the target task. The mean dice metric is used for evaluating the accuracy of the target segmentation task. We run cach method ten times the larget task and report the average and standard deviation performance over all runs as well as statistical analyses based on independent two-sample t-test.
4.5
Cell counting
A naive method for aulomatic counting of cells from segmentation predictions involves computing the con- nected components within the masks and considering the number of connected components as the cell count. However, this approach may not be accurate due to the presence of overlapping cells that share boundaries. To address this issue and improve counting accurscy, we first calculate the minimum and average cell size using the ground truth for the training data. Then, take the model's predictions (segmentation masks) and extract the connected components within them; each connected component represents one (in the case of overlapping colls). We then filter out components that are smaller than the minimum coll size. For the remaining components, we count cells by dividing the cell size by the average cell size.
cells
5 Experiments and Results
5.1 Self-supervised models provide more generalizable representations than supervised pre-trained models
Experimental setup. In this experiment, we evaluate the transferability of three popular SSL methods using officially released models, including DeepCluster-v2 (Caron et al., 2021), Barlow Twins (Zbontar al., 2021), and SwAV (Caron et al., 2021). All SSL models are pre-trained on the ImageNet dataset and employ ResNet-50 backbone. As the baseline, we consider (1) training the target model from random initialization (without pre-training) and (2) transfer learning from the standard supervised pre-trained model on ImageNet, which is the de facto transfer learning pipeline in medical imaging (Hosseinzadeh Taher et al., 2021). Both baselines benefit from the same ResNet-50 backbone as the SSL models.
Results. Table displays the results, from which we draw the following conclusions: (1) transfer learning from the supervised ImageNet model lags behind training from random initialization. We attribute this inferior performance to the remarkable domain shift between the pre-training and target tasks. Tn

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Pre-training Method
Pro-training Datasct
Dico (%)
Random
67.2241.24
Barlow Twins
ImageNet
79.50:12.02
SwAV
ImageNet
80.83#1.17
Barlow Twins
In-domain
70.92:15.41
Barlow Twins
ImageNet--In-dooee
$1.73+1..03
Table 2: Comparison of pro-training dataset for self-supervised learning.
particular, supervised ImageNet models are encouraged to caplure domain-specifie semantic features, which may be inefficient when the pre-training and target data distributions are far apart. Our observation is in line with recent studics (Raghu et al., 2019) on different medical tasks suggests that transfer learning from supervised ImageNet models may offer limited performance gains when the larget dataset scale is large enough to compensate for the lack of pre-training.
(2) Transfer learning from self-supervised models provide superior performance compared with both training from random initialization and transfer learning from the supervised ImageNet model. In particular, the best self-supervised model (i.c. SwAV) yields 1.3% and 2.27% performance boosts compared with training from random initialization and the supervised ImageNet model, respectively. Our statistical analysis based on independent two-sample t-test demonstrates the significance (p < 0.05) of the gain provided by SwAV compared with random initialization. Intuitively, self-supervised pre- trained models, in contrast to supervised pre-traine rreatraieed models, encode features that are not biased to task-relevant semantics, providing improvement across domains. Our observation in accordance with previous studies (Hosseinzadeh Taher et al., 2021) demonstrates the effectiveness of self-supervised ImageNet models for medical applications.
5.2 Self-supervised models provide superior performance in limited data settings
Experimental setup. We conduct further experiments to evaluate the advantage that self-supervised pre- trained models can provide for small data regimes. To do so, we randomly select 25% of the training data and fine-tune the self-supervised pre-trained models on this subset of data. We then compare the performance of supervipe models with training the target model from random initialization and fine-tuning the supervised ImageNet model.
Results. The results are shown in Table 1b. First, we observe that transfer learning from either supervised self-supervised pre-trained models offer significant (p < 0.05) performance improvements compared with training from random initialization. In particular, the supervised ImageNet model provides a 9.5% performance improvement compared to the random initialization of the target model. Moreover, self- supervised models- DeepCluster-22, Barlow Twins, and SwAV, offer 11.5%, 12.3%, and 13.6% performance boosts, respectively, in comparison with random initialization. These observations imply the effectiveness of pre-training in providing more robust target models in low data regimes. Second, we observe that self-supervised models provide significantly better performance than the supervised TmageNet model. Specifically, DeepCluster-22, Barlow Twins, and SwAV achieve 1.96%, 2.74%, and 4% performance boosts, respectively, compared to the supervised ImageNet baseline. Our statistical analysis based on independent two-sample t-test demonstrates the significance (p < 0.05) of the gains provided by the best self-supervised modols (i.c. SwAV and Barlow Twins) compared with the supervised ImageNet model. These observations restate the efficacy of self-supervised models delivering more generic representations that can be used for target tasks with limited data, resulting reduced annotation costs.
5.3 Impact of pre- vr-itranning data on self-supervised learning
Experimental sctup. We investigate the impact of pre-training datascts on self-supervised learning. To do so, we train Barlow Twins on three data schemas, including (1) SSL on the ImageNet dataset, (2) SSL on the medical dataset (referred to the in-domain), and (3) SSL both Tmt and in-domain datasets
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Metric
Score (%)
Method
Counting Error (%)
Precision
95.25
Connected components
21.66
Recall
95.49
Our approach
9.08
F1-score
95.31
(b) The results for automatic neuron counting error
(a) The results for counting precision, recall and F1- compared with expert human counting. score of our method human observers.
Table 3: Dopamine neuron detection and counting results
(referred as ImageNet--In-omman)) For ImageNet-<In-domall pre-training, we initialize the model with SwAV pre-trained on ImageNet, followed by SSL on our in-domain dataset. We fine-tune all pre-trained models for the neuron segmentation task using 25% of training data.
Results. Table 2 shows the segmentation accuracy measured by the Dice score (%) for different pretraining scenarios. First, we observe that pre-training on only in-domain dataset yields lower performance than pre-training on only the ImageNet dataset. We attribute this inferior performance to the limited number of in-domain pre-training data compared with the ImageNet dataset (1500 vs. 1.3M). Moreover, we observe that the best performance is achieved when both ImageNct and in-domain datasets are utilized for pre-training. In particular, ImageNee--In-oommin pre-training surpasses both in-domain and ImageNet pre-trained models. Our statistical analysis reveals that pretraining on ImageNet followed by pretraining In-domain data provides significant boosts (p < 0.05) compared with both in-domain pretraining and ImageNet pretraining standalone. These results imply that pre-training on ImageNet is complementary to pre-training on in-domain medical datasets, resulting in more powerful representations for medical applications.
5.4 Dopamine neurons detection and counting
Experimental setup. The DA neurons segmented by the model were compared to the DA neurons detected by biologist in the same tissue section from the blind data-set. The biologist detected the DA neurons and counted them manually an image analysis platform ImageJ. The output from the model was overlaid with the manually detected cells and based on the color coding of the DA neurons by the model, the true positive (TP), false positive (FP) and false nogative (FN) were calculated by the biologist. We calculated precision, recall and F1-score metrics for the detected neurons the test. images. In these measures, TP is the number of neurons successfully detected by the model; FP is the number of neurons detected by the model but are not actually neurons; and FN is the number of neurons not detected by the model. We further compare the performance of our method in neuron counting to human counting. To do so, calculate the percentage error between the total number of neurons counted by our method and human counting. We also conduct an ablation study to illustrate the superiority of our cell counting method over the naive approach of counting cells by the number of connected components in the images.
Results. The performance metrics for neuron detection are shown in Table 3a. seen, our method can effectively detect dopamincrgine neurons in whole-slide digital pathology images; in particular, our approach achieves a precision, recall, and Fl-score of 95.25%, 95.49%, and 95.31% 1,, respectively. Moreover, Table 3b presents the neuron counting results against human counting. As seen, automatic counting of the cells through computing the connected components within sogmentation masks yields an error rate of 21.66%, while incorporating the connected components' sizes in counting significantly decreases the error rate to 9%. These results demonstrate the elfectiveness of our approach in handling overlapping neurons and providing a reliable automatic system for neuron counting.
5.5 Comparison with state-of-the-ar cell counting methods
Experimental setup. We compare our model's efficacy in counting the dopamine neurons with the latest generalist cell segmentation model, i.e. Cellpose (Stringer et. al., 2021). To do so, first employ zero
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Model aided cell counting
400
Pearson
0.9793
300
95% confidence interval
0.9442 to 0.9924
R
0.9591
200
P-value
100
(two tailed)
0.0001
P-value summary
****
Significant (alpha=0.05) 05)
100
200
300
400
Ground Truth (Manual counting)
Figure 2: Correlation plot. depicting the number of DA DA neurons counted by our developed model. A blind data set used to count the neurons from 18 brain sections (i.c. the number of XY pairs used in the plot) stained with TH staining to identify the DA neurons and Nissl stain to stain the brain tissue. The sections for this study were chosen from multiple animal studies.
counted by a biologist vs. the number of
400
Pearson r
Callposa model
300
confidence interval
0.8538
R
0.8917
200
P-value
100
P (two-tailed)
0.0000
P-yal ue summary
Significant lalpha-0.00
Yes
200
Ground Truth (Manual counting)
Figure 3: Correlation plot depicting the number of DA neurons counted by biologist the number of DA neurons counted by the Cellpose model. A blind data set was used to count the neurons that were previously used to analyze model efficiency in Figure 2.
padding to make the size of the test images equal to power of 512. Then, we divide the test images into non-overlapping 512x512 patches and then feed patches to the network. We then assemble the model's predictions for image patches to generate the prediction for the whole image. To examine the model's efficiency in counting DA neurons, biologist counted the cells manually, referred to as counting ground truth (GT), in the same section (blind dataset). We then ran correlation statistics to measure the R between the predictions of model and the GT. We additionally compared the GT to the Cellpose's results via correlation statistics. Finally, the counting results for DA neurons from our model, GT, and the Cellpose were plotted head to head lo examine the elliciency of our model.
Results. Figure 2 shows the correlation plot between the GT and our model's counted DA neurons. R² of 0.95 with a pavuu < 0.0001 was achieved by our model in correlation statistical analysis. Figure depicts the correlation plot between the GT and Cellpose. seen, under the same parameters and dataset, Cellpose achieved R² of 0.89 with p-value 0.0001 in the correlation statistical analysis. deeper analysis of the data in Figure 4 revealed that Cellpose undercounted the DA neurons in three sections highlighted by arrow when compared to the GT counts. By contrast, our model was able to count the cells with higher accuracy when compared to the GT in the three sections under the study.
5.6 Quantitative results
Figures and presents the visualization of the DA neuron segmentation results in test images using our best model. As seen, our method can effectively detect and segment the dopaminergic neurons of varying size and shape. Our quantitative results in Table 1, together with the qualitative results in Figures 5

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Published in Transactions on Machine Learning Research (10/2023)
400
AMOVA Summary
Dopamminergic neurons
0.1309
Pvolue
0.87%6
300
Tupluu sumt taey
R.s.
Sigrliianti dilli moens jpst (6))
No
200
0.005108
Banferron'ss
100
comparisons
Summary
Pivalue
Our model vs. GT
48.56
-57.54 to 50.43
No
n..
>0.3533
Our modal vs. Callosse
4.278
54.71 to 63.26
No
as.
>0 30.9999
Our model
Ground Truth Cellpose
Figure Comparison of DA neuron counting between our model and the Cellpose baseline in individual sections. The green, black, and brown dots depict the cells counted by our model, ground truth (GT), and Cellpose, respectively. The red lines indicate the comparison between GT and our model. The blue lines indicate the comparison between GT and the Cellpose. Sections were selected from the blind dataset.
Image
Ground Truth
Prediction
Figure 5: Visualization of Mouse brain 2D Image depicting DA neurons in the SN and segmentation results produced by our method.
and 6 demonstrate the capability of our framework in providing an effective solution for segmentation of dopaminergic neurons.
5.7 Analyzing phenotypic characteristics of DA neurons
Experimental sctup. Extracting additional information on the state of the individual dopaminergic neu- rons, such as the TH intensily, can help biologists with additional data for analytic lasks in practical settings. In particular, the TH intensity is indicator of dopaminergic neuronal health. As such, measuring the TH intensity in individual cells can help to categorize the cells in compartments defining the effect of dis- ease pathology progression in different mouse models. The information about the health of the neurons is extremely valuable to determine the efficacy of drugs in PD) preclinical trials. Studies have shown that TH intensity is lost in specific DA neurons in PD transgenic and injection animal models but the overall analysis of such observation manually next to impossible (Kawahata & Fukunaga, 2020; Pajarillo et al., 2020; Vecchio et al., 2021). Thanks to our automated method for segmenting the DA neurons, we provide a mcasurement of individual cell TH intensitics, facilitating deeper analysis into the biology of DA neuronal loss and understand the mechanism through which this loss happens and what it suggests in terms of PD pathogenesis. To do so, measured the TH intensity after converting the images into grayscale (8-bit, 0-255 range). The lower the number or closer to 0, the darker the stain is. The higher the number or closer to 255, the lighter the stain. The TH intensity was measured on ImageJ, which platform for analyzing digital images.
Results. Figure 7 shows the TH intensity (brown color) individual DA neuronal cell bodics different gradients. The gradient was obtained by measuring the TH intensity for an entire dataset and splitting it into 5 different groups and visual and numerical data obtained for each neuron.
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Published in Transactions on Machine Learning Research (10/2023)
Correct
Ground truth only
Our method only
Ignored regions
Figure Visualization of cell segmentation results yielded by our model compared with ground truth
Cell a
Mean Grayscale Intersity
134.11
2
135.09
144.81
142.19
110.83
170-255
140-170
110-140
80-110
0-80
Figure 7: Measurement of cell TH intensity. The lop panel shows an example image and the color overlay of mean intensity measured in 8-bit grayscale. The bottom panel shows the intensity color legend, magnified view of several cells outlined in the black box above and table of mcan grayscale intensity value for cach cell.
6 Ablation Experiments
Experimental setup. We conduct extensive ablation experiments on different data augmentation tech- niques and network architectures. We examine seven different combinations of transformation that are commonly used in the literature, including (1) no augmentation (mode 1), (2) Flip, Rotation, RandomBright- nessContrast, and RandomGamma (mode 2), (3) Flip, Rotation, RGBShift, Blur, GaussianNoise (mode 3), (4) Flip, Rotation, RGBShift, Blur, GaussiannNoise, RandomeeesizddCrgg (mode 4), (5) Flip, Rotation, RG- BShift, Blur, GaussianNNise, RandomResizodCrop Elastic Transformation (mode 5). (6) Flip, Rotation, BrightnBighht
RandonGamma, RGBShift, Blur, Gaussian Random 6), and (7) Flip, Rotation, RandomBrrghttnss Random RrsizedCrop, Elastic Transformation (mode 7). For network architectures, examine U-Net and DeepLabV3+. In ablation experiments, all models are initialized with SwAV pre-trained model and fine- tuned with 25% of data.
(mode RandomGamma, RGBShift, Blur, GaussianNoise,
Results. Table 4 shows the results of different data augmentation techniques. According to these results, the lowest performance comes from mode 1 (no augmentation). highlighting that combining pre-training with data. augmentation techniques yields more accurate segmentation results for downstream tasks with limited amounts of data. Additionally, the combination of Flip, Rotation, RGBShift, Blur, GaussianNoiss,, RanzooRResizedCrop (mode 4) provides the best performance among all data augmentation approaches. This implies that color transformations such as RGBSShift Blur, and GaussianNoise can help the deep model in glcaning more generalizable representations. Furthermore, a comparison of the results obtained by modes 3 and 4, the latter of which includes an additional RandomBesszzeCCrop reveals that random cropping significantly contributes to performance improvements. Moreover, a comparison the results obtained by modes 4 and 5, the latter of which includes an additional clastic transformation, demonstrates that clastic transformation has negative impact on performance; the same observation can be drawn from the comparison of modes 6 and
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Published in Transactions on Machine Learning Research (10/2023)
Data Augmentations
Mode
Dico(%)
Brightness Contrast
RGB Shift
Gaussian Noise
Random
Plip
Rotation
Gamma
Blur
Blastic
Crop
78,96
2
80.83:1.117
4
80.6411.06
/
81.9440.74
v
79.97+2.78
6
81.30++0.93
v
80.43+1.188
Table Comparison of different data augmentations.
Network Architecture
Dice(%)
DeepLabV3+
81.53:0.77
U-Net
81.94+0.74
Table 5: Comparison of different segmentation architectures.
Table 5 presents the results of different network architectures for neuron segmentation task. As seen, U-Net, originally was designed for medical segmentation tasks, provides superior performance over VbepLabV3+.
7 Conclusion
In this paper, developed robust machine learning model that can detect and count the DA neurons reliably in independent animal studics. This is an immediate requirement in the field of PD rescarch to accelerate the in-vivo screening of potential drugs so that more drugs can be taken into the clinic for human trials. The existing manual counting or stereology based method unable to keep up with the number of studies currently conducted in different labs focusing on this area. Additionally, it also suffers from human bias which makes the data interpretation extremely cumbersome. The study framework is established on a self-supervised learning paradigm to combat the lack of large-scale annotated data for training deep models. We also realized that using segmentation based methods facilitated DA neurons which the existing machine learning models are implementing, In addition to counting the DA neurons, we were able to obtain characteristics of the DA neurons which is very valuable to understand the health status of individual DA ncurons on a histology slide. The loss of dopaminergic ncurons determines the exlent of pa hogennsssss
go beyond counting the number of
PD. Our model determines the number dopaminergic neurons by counting the number of cell body/ soma of dopaminergic neurons. In addition, the ability to measure the TH intensity individual cells (indicator of dopaminergic neuronal health) helps us categorize the cells in compartments defining the effect of discase pathology progression in different mouse models. The information we obtain the health of the neurons is extremely valuable to determine the efficacy of drugs in PD preclinical trials. Studies have shown that TH intensity is lost in specific DA neurons in PD transgenic and injection animal models but the overall analysis of such observation manually is next to impossible. With our method, we can get the cumulative data to dig deeper into the biology of DA neuronal loss and understand the mechanism through which this loss happens and what it suggests terms of PD pathogenesis. There are additional challenges consider such as limited datasets, tissue section thickness, image resolution and overlapping cells but our model has demonstrated very high efficiency taking into consideration all these factors. With the advancement in machine learning and biology, these models will improve and provide solutions to the ever increasing demand for data-analysis in rescarch biology. Our experimental results suggests that our method can be extrapolated to other species that are used as animal models in PD. With the addition of more labeled data, we could go deeper in understanding the biology of DA neuronal loss by capturing the changes which are visible or sometimes not visible the human eyc. To summarize, this method will be very useful to shorten the time needed analyze loss of DA neurons in animal studies and accelerate the drug discovery of PD.
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June Published in Transactions on Machine Learning Research (10/2023)
8 Acknowledgements
We would like to thank Oded Foreman, Amy Easton and William J. Mcilandt for providing the resources to facilitate this study. We appreciate the assistance from Kimberly Stark for animal study management and Renee Raman for imaging management. We would also like to thank Neuroscience Associates Inc. (Knoxville, TN, USA) for providing the tissuc processing infrastructure to conduct this study. We express our gratitude to Labelbox Inc. for providing us the infrastructure to manage and label images. Labelbox is a prominent data-centric AT platform that empowers teams working with generative AT and large language models (LLMs) to infuse these systems with the optimal balance of human guidance and automation.
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15


Reviews:
[{"review_id": "bHvKoG4MDh", "reviewer": "Reviewer_H8SG", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. It looks novel and promising to leverage natural images and medical data to improve segmentation and quantification of medical images using self-supervised learning.\n2. Ablation studies are pretty solid and comprehensive to reveal characteristics of the proposed method.\n\nWeaknesses:\n\n1. In the experiment section, my major concern is that it lacks comparisons with SOTA methods. Ideally, it is encouraged to have comparisons with SOTA methods from both traditional medical image segmentation area and deep learning based methods. So it will show improvement and advances of the proposed method in this area.\n2. Because medical images show quite different characteristics\tregarding imaging modalities, object shapes, resolution, colors, etc., I am wondering why using natural images for self-supervised pre-training helps the medical image segmentation problem. Any rationale behind it the authors could illustrate more?\n3. In sec. 3.1, what is the number of histology digital images used in this study? I read 30k TH positive DA neurons.\n4. Sec. 4, the authors observed self-supervised models have better performance than the supervised ImageNet model. Usually on the same dataset and prediction tasks, supervised method should have better performance than self-supervised learning methods. The author may want to clarify rationale behind their observations.\n5. What are the major difference between self-supervised learning methods employed in this paper and the contrastive learning?\n6. In page 8, the authors claim that “color transformations such as RGBShift, Blur, and GaussianNoise can help the deep model in gleaning more generalizable representations ”. I am wondering whether there are any shape deformations which can help to this problem? At least these neurons show oval shapes which may have some implications.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, the authors proposed a novel method based on self-supervised learning for segmentation and quantification of dopamine neurons in Parkinson’s disease. By leveraging info extracted from natural images and medical data, they use self-supervised pre-training to transfer these knowledge to a fine tuned supervised deep neural nets on labeled medical data. Comprehensive ablation studies were conducted to show contributions of each component of the proposed method.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See comments on weaknesses.", "broader_impact_concerns": "1. This paper is more like an application paper which apply self-supervised deep learning for segmentation and quantification of dopamine neurons. I do not see significant innovation regarding methodology. So it is more suitable for journals in the medical imaging area, e.g. IEEE transactions on medical imaging.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "IHLF5taSvL", "reviewer": "Reviewer_Chm3", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The analysis of different SSL strategies is interesting. The paper provides a nice insight into exploiting SSL strategies for a particular task in a limited labeled setting.\n\nWeaknesses:\n- Novelty: I think novelty is the main weakness of the paper. The paper evaluates the combinations of different SSL strategies a medical imaging task; however, it doesn't propose a technical contribution.\n- Shortcoming of the experimental design: I think there are a couple of crucial shortcomings of the paper due to experimental design.\n1- The experimental results were presented on only a single in-house dataset. Therefore, it is difficult to generalize the paper's conclusions to different datasets, even for the same problem. I would expect to see experiments on multiple datasets to reach the conclusions drawn in the paper. Additionally, using only an in-house dataset restricts the reproducibility of the experiments.\n2- In Sec 3.3., the paper mentions that there are 1500 images in total, and \"all\" images are used in the SSL training. Training/test/val splits are only mentioned for the supervised fine-tuning part. As far as I understand, the test images used in the supervised training part were also used in the SSL part. This is quite unusual to me since the model should not see the test images at any stage of the training, even during the unsupervised one.\n3- The paper mentions some methods for neuron counting in the related work section but doesn't present any comparison.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper explores different self-supervised learning strategies for segmenting and quantifying Dopamine Neurons on histopathology images. The strategies explored in the paper include SwaV, Barlow Twins, Deep Cluster, and supervised pretrain on ImageNet. The paper demonstrate that the best-performing strategy steps are 1) SSL pretraining on ImageNet using BarlowTwins, 2) SSL training on the medical data using Barlow Twins, 3) fine tuning with the limited labeled dataset.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "The paper applies existing SSL methods for segmenting and quantifying Dopamine Neurons on histopathology images and does not present a methodological contribution. Therefore, I believe it is more suitable for an applied venue. Please see my comments about the design of the experiments above for potential submission to such a venue.", "broader_impact_concerns": "I don't see any ethical implications of the work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "qIicFMQJHw", "reviewer": "Reviewer_8wRy", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n- The paper is written well and organized.\n- The paper studied many design choices and provided a good view on how each component contribute to the final results.\n\nWeakness: \n\n- The machine learning related contribution in the paper is the segmentation system on the specific domain of DA neurons in histopathology images. The observations the presented for transfer learning e.g. pretrained model is better than random initialization, additional pretraining on in domain data is better than the image-net pretrained one, et al. have been seen in other studies of different computer vision applications. Although I believe this paper is valuable and interesting for researchers in the medical imaging and histopathology domains, I am not sure how interesting it will be for TMLR audience.\n- Some clarification and details are needed for better evaluated the correctness of the paper\n    - Authors claims that the system “provides the phenotypic characteristics of individual dopaminergic neurons as a numerical output”. What exactly are phenotypic characteristics? Does it refer to the mean intensities of cells or anything else?\n    - In the qualitative results, the paper states that“In Figure 6, the statistics shows there is not significant changes between the DA neurons counted by the model or Cellpose when compared to GT. Deeper analysis into the data shows that Cellpose had a significant difference from GT in three sections but our model was able to detect the DA neurons with higher accuracy.” How should we understand this? They sounds contradictory? Which “three sections” refer to?\n    - There are two different descriptions for how to build the test set:  1.“For supervised learning, we randomly divided the dataset into training (70%), validation (10%), and testing (20%).” 2. “The blind test data-set used for analyzing model’s efficiency was a separate animal study in which the model has not been directly trained on the study group.” Which one is the correct one, random split or a separate study?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This submission presented a system that automatically count the number of dopaminergic neurons. The system consist of a segmentation model that finetune a pretrained model on digital microscopy images with manual segmentation annotations. The paper studied the source of the pretrained model in terms of pretraining methods (supervised pretraining and a few self-supervised algorithms), pretraining data domain. The paper found that in the 25% fine-tuning data setting, self-supervised pretraining on ImageNet and then in-domain data make the best segmentation model. More ablation studies on data augmentation choices and network architectures are provided. The counting results from the deep learning system is compared with the manual counts as well as a generalist cell segmentation model, Cellpose.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "Besides the weakness mentioned above, there are few more suggestions for improving the paper.\n- For the ablation on data augmentations, it is easier to have table of checking marks for each augmentations for more clear comparison of each mode\n- It would be more convincing to show how the  segmentation performance changes translate into counting errors changes.", "broader_impact_concerns": "There is not a Broader Impact statement and I don't think it need one.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "SKgnUP3N2L", "reviewer": "Reviewer_coWh", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n- This paper seems to represent the first application of cell segmentation and counting DNNs for DA neurons. \n- The experimentation provided some relatively comprehensive results on the effect of pre-training on generic versus in-domain dataset on downstream tasks\n\n\nWeaknesses\n\n- A major concern is the unclear methodological contribution of the presented work with respect to existing cell segmentation and counting approaches. It was indicated that these existing methods have limitations when applied on DA neurons in animal models of PD, but it was not clearly explained what may be the limitations and challenges, and how the presented works overcome these challenges. In another word, it is not clear why the presented approach can achieve what existing methodologies cannot achieve. \n\n- A related concern to the above is the lack of considerations on alternative methods that can be applied to this problem during experimentation. Cell-counting was compared to Cellpose, but it was not clear what is the underlying methodology for Cellpose. More importantly, among existing cell segmentation and counting models, none of them seem to be considered. Again this is related to the comment above — the authors need to either explain why none of these methods can be applied to this problem, or include some of them as baseline methods in order to demonstrate the contribution of the presented method.\n\n- Another major concern is the lack of discussion and consideration on the human annotator(s) and the potential labeling uncertainty associated with the human annotator(s). It was not clear how many biologists are involved as the human annotator(s). If multiple are involved, it should be discussed how inter-operator disagreements are addressed to reach consensus in labeling. If only one human annotator is involved, it should be explicitly described and limitation discussed.\n\n- Many of the results presented, such as those in Table 1, seem to show differences that are not statistically significant (the gain in mean seems to be smaller than 1 std). This should be discussed, and statistical tests should be done for significance. \n\n- For results on pre-training (Table-2), is there a reason that the effect of in-domain pre-training and imageNet—>In-domain pre-training is not tested on SwAW, considering that SwAW seems to give more favorable results on ImageNet alone?\n\n- For the baseline for cell-counting, it was not clear what is the “naive approach of counting cells by the number of connected components”. Is it based on the segmentation results of the presented approach, but only differ in not using the method described in section 3.7? Or is it an entirely different approach?\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents an approach for segmenting and counting dopaminergic (DA) neurons in whole-slide digital pathology images.  The method is based on self-supervised pre-training first on ImageNet, followed by fine-tuning on microscopy images, and finally supervised learning. Experiments evaluated the effect on the fine-tuning stages as well as the performance on cell counting.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "\n- The authors should consider clearly outlining the challenges that exist in existing cell segmentation and counting methods that preventing them from being directly applied to the DA neuron segmentation problem.\n\n- The authors should consider including representatives about the above methods to demonstrate why the presented method is needed or favorable.\n\n- The authors should add details on human annotators and annotating uncertainty.\n\n- The authors should add statistical test for difference on the presented quantitative results.\n\n- The authors should provide clarifications on the last two questions listed in the section above.\n", "broader_impact_concerns": "There is no concerns on ethical implications of the work. ", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)