# High-throughput analysis of dislocation loops in irradiated metals using Mask R-CNN

Camilo A.F. Salvador, Thomas Bilyk, Antoine Dartois, Lisa Vessier-Alary,

Estelle Meslin, Mihai-Cosmin Marinica

► To cite this version:

Camilo A.F. Salvador, Thomas Bilyk, Antoine Dartois, Lisa Vessier-Alary, Estelle Meslin, et al., High-throughput analysis of dislocation loops in irradiated metals using Mask R-CNN. Micron, 2026, 201, pp.103927. 10.1016/j.micron.2025.103927. cea-05353744

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Full length article

# High-throughput analysis of dislocation loops in irradiated metals using Mask R-CNN

Camilo A.F. Salvador<sup>10*</sup>, Thomas Bilyk, Antoine Dartois, Lisa Vessier-Alary, Estelle Meslin, Mihai-Cosmin Marinica

Université Paris-Saclay, CEA, Service de recherche en Corrosion et Comportement des Matériaux, SRMP, 91191 Gif Sur Yvette, France

## ARTICLE INFO

Dataset link: https://github.com/ai-atoms/crem e

**Keywords:**

Dislocations

Deep learning

Image segmentation

High-throughput

Irradiated metals

## ABSTRACT

Advances in transmission electron microscopy under extreme conditions have enabled in situ experiments to capture vast amounts of data on defect evolution. On the other hand, computer vision models such as Mask R-CNN have become popular in the last few years, enabling fast and accurate segmentation of images of different natures. In the present work, we propose a workflow to label, segment, and analyze irradiation-induced defects in TEM images using Mask R-CNN. The work focuses on interpreting bright-field (BF) videos recorded during the irradiation of three different metallic materials. After establishing a baseline dataset based on austenitic stainless steel 316L, we tested small and large models as the backbone of Mask R-CNN and different hyperparameters for training them. Our best model predicts the areal density of defects in 316L with 83.6 % accuracy, measured by a custom metric which assesses cumulative defect area using an ellipsoidal approximation. We tested the generalization limits of the trained model to ensure accurate estimations of key physical metrics, including the foreground fraction occupied by defects, the number of detected particles, and their relative sizes — all of which exhibit relative errors below 5%. At last, the model helps interpret videos concerning two similar irradiation experiments: one with the 16Cr-37Fe-13Mn-34Ni (at. %) alloy, and another with pure Cr. The model's segmentation clearly captures the different nature of defect evolution between different materials, as expected. Moreover, the proposed workflow not only enables consistent, real-time analysis of small defect loops during in situ TEM experiments but also generates the quantitative data needed to refine mesoscale models.

## 1. Introduction

Transmission Electron Microscopy (TEM) is a critical tool in materials science, equipping researchers to observe microstructural features at the nanoscale. In situ TEM, in particular, enables direct observation of irradiation-induced defect evolution and provides insight into the dynamic processes occurring within materials subjected to extreme environments (Arakawa et al., 2007; Rahman et al., 2020; Barr et al., 2022). For example, to simulate radiation damage in conditions analogous to nuclear reactors, in situ ion beam radiation experiments are employed to replicate displacement damage, elevated temperatures, and even the introduction (or generation) of new transmuted elements (Parrish et al., 2021; Bawane et al., 2024). Furthermore, the recent emergence of in operando TEM is expected to generate a large volume of data, with increased complexity (Hwang et al., 2020; Alloyeau et al., 2025). On the other hand, the vast amounts of data acquired via in situ TEM present significant challenges for analysis and interpretation (Taheri et al., 2016). Manual counting and characterizing defects, such as dislocation loops, precipitates, bubbles, or

cavities, are time-consuming and prone to inconsistencies due to human subjectivity and observer bias (Chen et al., 2023). For instance, round robin studies have shown that evaluations from a single researcher can vary significantly (Bruno et al., 2023), emphasizing the need for strategies to reduce bias in defect analysis. Standardized methods for TEM image analysis, which often involve illumination-correction routines (Zavodov, 2021), low-frequency filtering (Foley et al., 2025), and threshold-based segmentation (Groom et al., 2018), can be effective for a few selected frames. However, finding a single set of parameters that generalizes to a series of images with varied background conditions is nearly impossible. This limitation is further aggravated in low-resolution bright-field TEM, where background intensity often mimics defect features, leading to inconsistent or deceptive segmentations. Consequently, developing automated image analysis tools is imperative for improving efficiency, repeatability, and accuracy in defect quantification.

Over the past decade, Convolutional Neural Networks (CNNs) and deep learning techniques have become powerful tools in computer

<footnote>* Corresponding author.
E-mail address: camilo.fernandessalvador@cea.fr (C.A.F. Salvador).</footnote>
vision (Krizhevsky et al., 2012). As CNNs became deeper, performance did not always improve. Networks with over 50 layers often underperformed due to optimization issues: gradients during backpropagation either vanished or exploded, leading to unstable training (Glorot and Bengio, 2010). Even methods like ReLU activations and batch normalization could not completely address these problems (Ioffe and Szegedy, 2015). ResNet, introduced in the end of 2015 (He et al., 2016), addressed the vanishing gradient problem through residual connections enabling the training of architectures with over 150 layers. ResNeXt (Xie et al., 2017) further improved ResNet's efficiency by introducing grouped convolutions (i.e., 'cardinality') while maintaining computational viability. These advancements laid the foundation for Mask R-CNN (He et al., 2017), an architecture capable of simultaneous object detection, classification, and pixel-level segmentation. In recent years, the adoption of computer vision models such as Mask R-CNN in materials science has increased significantly (Jacobs, 2022).

Li et al. (2018) pioneered the application of CNNs to identify multiple defect types in scanning transmission electron microscopy (STEM) micrographs of irradiated ferritic alloys; the on-zone STEM mode was selected due to a reduced background. In the [100] zone axis, the 2-D projection of loops with a Burgers vector $a/3 \langle 111 \rangle$ are imaged as black-on-white ellipses. Later, Jacobs et al. (2022) successfully categorized (100), (111) loops and black dots with the aid of Mask R-CNN. More recently, Bilyk (2024) demonstrated that a similar approach could provide insights into the distributions of overlapping perfect, faulted, and perfect-on-edge dislocation loops in irradiated multi-principal element alloys (MPEA) alloys (Bilyk et al., 2024). These past studies, however, were primarily focused on the pixel-level segmentation of high-resolution TEM images, which are usually acquired in post-mortem analysis (or ex situ TEM).

Concerning in situ TEM, recent studies also demonstrated the potential of deep learning for image denoising, object detection, and segmentation. For instance, Moncomble et al. (2025) employed a U-Net-like (Ronneberger et al., 2015) architecture trained on synthetic TEM data to enhance nanoparticle segmentation in liquid-phase TEM videos by reducing the noise on experimental images. Similarly, Govind et al. (2024) applied a U-Net++ model (Zhou et al., 2018) with a ResNet50 backbone to denoise TEM images of dislocation lines in various metals, leveraging synthetic noise for training. While these approaches focus on denoising, other works have explored end-to-end deep-learning frameworks for TEM video analysis (Saaim et al., 2022). Notably, Shen et al. (2021) proposed a framework integrating YOLOv3 (Redmon and Farhadi, 2018) for object detection (no segmentation) and Trackpy (Allan et al., 2014) for trajectory analysis to study the size and movement of black dots in body-centered cubic (BCC) Fe-18Cr-3Al videos irradiated with 1 MeV Kr⁺⁺ ions at 320 °C (Shen et al., 2021). Likewise, Burns et al. (2024) developed a deep learning pipeline to analyze defect evolution in situ TEM videos of Cu and Pd under varied irradiation doses (Burns et al., 2024). While previous studies have shown that end-to-end approaches can effectively analyze videos of in situ irradiation experiments, our framework stands out in three key ways. First, we conduct a systematic evaluation of how different Mask R-CNN backbone architectures impact segmentation performance, a topic that has not been thoroughly explored in the existing literature. Second, we assess how the field of view (FOV) of the input images influences detection accuracy and defect statistics. Third, our framework is designed to focus on physically relevant defect characteristics, such as the number of particles and their size distributions, rather than just relying on general computer vision metrics. All data and methods detailed here are open-source and were made available to the community.

In short, this paper focuses on applying Mask R-CNN to detect and segment black dots in low-magnification, bright-field TEM images of irradiated metals acquired during in situ experiments. We propose a simplified workflow for post-processing video data of single-phase cubic metals and alloys subjected to similar radiation conditions. By

integrating machine learning with TEM videos, we aim to enhance the accuracy and efficiency of defect characterization in irradiated metals. Furthermore, our models provide massive data and quantitative insights into the relative resistance of novel metallic alloys to ion irradiation (Liu et al., 2022), informing the design of advanced radiation-resistant materials for the future generation of nuclear reactors (Yvon, 2017).

## 2. Methods

In the present paper, we inspect Mask R-CNNs with varied backbones readily available within the Detectron2 package (Wu et al., 2019). The models are trained on four annotated snapshots with 4096x4096 px (4K) resolution from a video with nearly 3600 unique features of the same class — the full field of view (FOV) is 1600 nm at the selected magnification. The video records an ion irradiation experiment of austenitic stainless steel 316L performed on a 100 µm thick electropolished foil at the JANNuS-Orsay facility (Gentils and Cabet, 2019), using a rastered 2 MeV Fe⁺ beam at a flux of $2.8 \times 10^{11}$ ions/cm² s, at 550 °C; the total fluence was $1 \times 10^{15}$ ions/cm². The experiment was recorded with a GATAN OneView camera at a FEI Tecnai G² 20 Twin TEM operating at 200 kV; the microscope was equipped with a custom heating holder adapted from a previous double-tilt GATAN model with an Inconel furnace that can be heated up to 800 °C. According to Iradina (Crocombette and Wambeke, 2019), with a displacement threshold of 40 eV, the damage rate of our experiment equals to $1.5 \times 10^{-4}$ dpa/s on the surface level, with an incidence angle of 56 degrees relative to the surface normal. As customary in a time series, we reserve a frame captured at an intermediate time for testing. A sample of the dataset is provided in the Supplementary Information. Furthermore, we propose two extra metrics to evaluate which models perform best at varied magnifications/fields of view. After training, the selected model helps interpret a second video concerning a similar experiment with the 16Cr-37Fe-13Mn-34Ni (at. %) Multi-principal element alloy (MPEA), with slightly different irradiation conditions: 30 min irradiation at 500 °C, the total fluence was reduced to $7.5 \times 10^{14}$ ions/cm². Finally, the model segments a video of pure Cr irradiated with 2 MeV Ni⁺ ions at 500 °C, at a damage rate of $6 \times 10^{-5}$ dpa/s, up to a total dose of 0.02 dpa. Fig. 1 displays a depiction of the proposed workflow, including the steps detailed below;

**Dataset engineering:** Manual annotations were performed as vector graphics using Inkscape (Inkscape Project, 2024); we annotated 2,978 unique loops in the four training frames and 631 unique loops in the testing frame. An example with one annotated snapshot and further details on the sizes of black dots in the chosen frames are depicted in Fig. 2 (a version of Fig. 2b without overlapping was included in the Supplementary Information). A Bezier curve with at least two points is attributed to each black dot identified in the original image; the curves roughly depict the form of a non-symmetrical ellipsis, and thus only a few points are needed to define each object. Afterward, the segmentation layer is exported with the full resolution; the image is converted to a binary mask and subjected to a watershed algorithm to ensure nearby features are separated, followed by a 1 px erosion and dilation to smooth the edges without changing the objects' size. The annotations are later compiled in a COCO (Common Objects in Context) dataset with the aid of the function *findContours* from torchvision (TorchVision maintainers and contributors, 2016);

**Data augmentation:** As nicely presented by Horwath et al. a significant challenge in image segmentation is to develop a model that can recognize interface pixels, which are remarkably different from the interior of an object, as contributing to the object and not the background (Horwath et al., 2020). The challenge is particularly hard when handling larger models, which can *memorize* the noise from the training dataset rather than the actual features — such undesired behavior eventually causes poor performance outside the training
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Flowchart of the framework dedicated to segmenting black dots in cubic metals and alloys. On the right-hand side, we propose a wrapper around Detectron2 to generate a COCO dataset based on manual annotations and train Mask R-CNN models with different backbones. The subsections of Methods are highlighted in *italics*. At the center, The custom evaluation of the trained models with two performance indexes integrates the assessment of such models. The best model parameters and weights are recorded and used in new in situ experiments. On the left-hand side, a new experiment (such as ES1 or pure Cr, which could be running on-the-fly) records images on a mapped hard drive. The frames are then pre-processed (rescaled and split into patches with 512x512 px) to adjust the field of view to the selected model. A prediction algorithm employs the given model to generate segmentation masks for each image and update graphical analysis concerning the areal density of defects and size distributions of that sample. With current configurations, the framework can process from 0.1 to 1.0 frames per second while running on a standard consumer-grade GPU with 6 GB of memory, depending on the chosen configuration. Running on high-performance computing can achieve 10 fps (with 512x512 pixels images).</figcaption></figure>

dataset (Wang et al., 2024). To circumvent this issue, there are two essential venues: (1) employing denoise algorithms (or models) alongside the segmentation, which was mentioned in the introduction, and has shown significant progress in biomedical micrographs (Lu et al., 2024), or (2) performing a massive data augmentation to ensure a substantial volume of training data. We choose route 2 in this work, given that it requires less fine-tuning and can be easily extended to other datasets due to the modularity of *torchvision* (TorchVision maintainers and contributors, 2016).

Therefore, to develop a model capable of segmenting TEM micrographs captured at different magnifications, we extract patches of varying sizes — 2048 × 2048 (2%), 1365 × 1365 (28%), 1024 × 1024 (31%), and 512 × 512 px (39%) — from the 316L original images. The corresponding field of view (FOV), defined as the physical dimension of an image, of such crops are 800, 533, 400, and 200 nm, respectively. One should note that larger crops are re-scaled to 512 × 512 px, the input resolution for the models. With this method, increasing the FOV reduces the resolution compared to the native resolution (at which the image was recorded). In addition, we apply data augmentation using *Compose* from *torchvision* (TorchVision maintainers and contributors,

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** (a) Depicts the frame from the 316L video with the most black dots (59.9 min) and the entire field of view (1600 nm). An example of the manual annotations (in red) was overlayed on the image. The size distributions of black dots among images of the reference dataset are presented in (b). The areal density of loops is 4.85 × 10¹⁴ m⁻², 3.40 × 10¹⁴ m⁻², 2.46 × 10¹⁴ m⁻² (test data captured at 45.9 min), 1.95 × 10¹⁴ m⁻², and 1.44 × 10¹⁴ m⁻² from 59.9 min (dose: 0.539 dpa) to 28.5 min (dose: 0.256 dpa), respectively.</figcaption></figure>

2016), where each transformation among the following is applied with a probability of 25%: *RandomRotation* (up to 45 degrees), *Horizon-talFlip*, *VerticalFlip*, *ColorJitter* (brightness and contrast, 20% variance), *GaussianBlur* (3 px kernel with sigma 0.2), and a custom *ElasticTransform*ation with displacements smaller than 25 px. As a result, the training dataset reaches 43,835 dislocation loops (995 images), while the test-ing dataset has 10,975 loops (256 images). By combining random operations such as varied magnification (field of view), rotation, blur, illumination, and contrast, we enable a more robust and scalable train-ing process that evades overfitting and can be reproduced in shallow or deep neural networks. Most of these augmentation operations have already been validated for treating TEM data (Lin et al., 2021). In addition, we included a local elastic transformation to mimic inherent distortions and drifts associated with EM imaging.

**Model optimization and training:** We chose Mask R-CNN (FPN-3x) as the base model, which includes a 3x repeated Feature Pyramid
Network backbone for enhanced multi-scale feature extraction compared to standard FPN versions. For simplicity, the model will be referred to as Mask R-CNN throughout the text. In the first stage, we sought to optimize the Mask R-CNN hyperparameters, especially those concerning the region proposal network, to better fit our problem. Optimizing non-maximum suppression (NMS) parameters is essential since they determine proposal selection. A higher PRE_NMS_TOPK (12,000 for training and 4000 for testing) increases recall by considering more candidates, while POST_NMS_TOPK (6000 for training and 2000 for testing) improves precision and efficiency by retaining only the top proposals. The region of interest (ROI) box head and mask head were set to the standard resolution of 7 and 14, respectively. These parameters are thus carried over to all models regardless of the chosen backbone. Although we are only working with one class, the features are relatively small (there are between 2 and 200 objects in each image crop, depending on the field of view), and it is paramount to increase the number of detections per image (and also the batch sizes per image) to at least 512.

For training, we initialize our models using the standard pre-trained weights from the Detectron2 COCO instance segmentation baselines for Mask R-CNN (Wu et al., 2019). This approach leverages transfer learning, which provides faster and more stable training (Bilyk et al., 2024). A base learning rate of $1 \times 10^{-3}$ was chosen; warm up interactions and decay of the learning rate were sampled but not used in the final models. We employed the standard Mask R-CNN loss from the Detectron2 framework, which consists of a sum of classification loss, bounding box regression loss, mask loss, and region proposal network (RPN) losses. In short, it includes a cross-entropy loss for object classification, a smooth L1 loss for bounding box refinement, a binary cross-entropy loss for mask prediction, and both classification and regression losses for the RPN (classification losses are ignored in the case of one class training). Our final validation loss for most models is around 1.3, and the proportion of false negatives and positives is almost the same, around 18%. Such results are obtained after approximately 20.000 training epochs.

## 3. Results and discussion

**Metrics and evaluation:** Table 1 compiles the prediction performance of the models with each backbone in three FOVs: 200, 400, and 533 nm. These metrics were obtained with the original testing dataset concerning the 316L experiment. First and foremost, it is worth reminding the reader that the model always receives 512 px images, so increasing the FOV over 200 nm results in a loss of information. Secondly, we must note the distinctive characteristic of our case compared to the standard use of Mask R-CNN: the low resolution of the objects. In some cases, the annotated and detected contours may be reduced to a few pixels, artificially increasing their intersection over the union (IoU). Ultimately, the standard mean average precision (mAP) is demonstrated to be a poor metric in our context, contrary to what is commonly observed in the literature (Girshick et al., 2014; Lin et al., 2014). In general, the models achieve similar testing mAP values around 30%, which are constrained by the nature of the dataset. When image crops containing low-confidence regions (e.g., line defects) are excluded from the testing set, the mAP increases significantly, ranging from 65% to 81%, depending on the chosen backbone and field of view (FOV). However, in order to tie model performance to physically meaningful characteristics of the system, we propose two additional performance indexes based on the segmented foreground percentage (F), the number of particles detected (N), and their sizes (S). We assume particles to be ellipses, and the sizes (S) are calculated as the average between the ellipsis major (D) and minor axes (Dm). With the relative error ($Y_{re}$) between predictions ($Y_{pred}$) and ground truth ($Y_{gt}$) for a given property (Y) being:

$$Y_{re} = \left( \frac{Y_{pred} - Y_{gt}}{Y_{gt}} \right) \times 100 \qquad (1)$$

The first performance index ($P_1$) is simply the harmonic mean of errors, attributing an increased weight factor to the size of particles (factor 2 for each axis, i.e. 4 overall):

$$P_1 = 100 - 6 \left( \frac{1}{1 + |F_{re}|} + \frac{1}{1 + |N_{re}|} + \frac{4}{1 + |S_{re}|} \right)^{-1} \qquad (2)$$

Performance index 2 ($P_2$) represents the relative error (Eq. (1)) of the total area of defects ($A_{tot}$, which in turn can be estimated for ground truth and predictions assuming particles with the area (A) an ellipsoid shape. This metric combines average N, D and Dm, resulting in something similar to the foreground percentage but more precise.

$$A_i = \pi \cdot D \cdot D_m \qquad (3)$$

$$P_2 = A_{tot} = \langle N \rangle \cdot \pi \cdot \langle D \rangle \cdot \langle D_m \rangle \qquad (4)$$

The proposed metrics make interpreting the results from Table 1 much simpler than with mAP, as they provide physical details on the defect distributions. Overall, models perform better with the smaller FOV, in our case 200 nm; the main advantage of this parametrization is that the images are not rescaled, resulting in no loss of information. The only disadvantage of such an approach (aside from the time to process more images) concerns loops divided into two (or more) when the field of view is reduced, also known as cropping artifacts. Even so, our findings demonstrate that the number of accurately segmented loops is significantly greater than those inadvertently divided. This allows the predicted foreground percentage and number of particles to be very close to the ground truth. Nonetheless, it is worth mentioning that the final models can receive images with resolutions higher than 512 x 512 px, and cropping artifacts can be avoided if the larger FOV image is accompanied by a higher resolution (e.g., 400 nm FOV images should be 1024 x 1024 px).

Although all models underestimate the size of the detected features, the relative error in size is also lower in the small FOV images. Chen et al. 2023 (Chen et al., 2023) showed relative errors in size are typically between 5 and 10% for cavities in Fe-Cr-Ni alloys, which seems compatible with the errors obtained in the present work (200 nm FOV). The authors also demonstrated that training Mask-CNN with different scales helps increase the recall and F1 score when segmenting ellipsoidal features. The F1 score is the harmonic mean of precision and recall, the default metric when false positives and negatives are equally important, ignoring only true negatives. In this study, we consistently observed testing F1 scores ranging from 0.65 to 0.70 across various backbone models. That happens because F1 does not adequately reflect the quality of the segmentation mask. In other words, while all the models detect a similar set of objects (yielding similar F1 scores), the boundary quality is notably enhanced with larger models, which predict particle sizes with greater accuracy.

Concerning the size of segmented objects, the region chosen to generate both training and test datasets displays line defects at which loop formation is preferred, but the segmentation of such loops is not consistent across different frames; in these regions, the main contrast comes from the line defect itself (these can be low-angle grain boundary, cracks, etc.), and it is often impossible to tell if the object consists of one sizeable black dot or several small dots overlapping each other. Given that the models are biased to small dots isolated in the matrix, a product of a homogeneous formation (majority of the dataset), they all tend to segment these regions into a chain of tiny black dots, and such behavior causes a reduction in the average mean size of the segmented loops. This can be verified when inspecting the last column of image crops in Fig. 3 (d, h, l).

When the FOV is increased (i.e. resulting in loss of information), the average foreground percentage (F) and number of detected particles (N) tend to drop. Lowering the threshold – defined here as the minimum confidence score required for a predicted mask to be considered valid – helps maintain these values in both ResNeXt-101 and ResNet-50. However, since most of the additional particles detected at lower
<caption>**Table 1**</caption>

Performance comparison of the best models. Run time refers to predicting all quadrants needed to rebuild the original 4K image. The average precision (AP) refers to (IoU=0.5). ($P_1$) refers to the harmonic mean of errors, assuming major and minor axes with a weight factor of 2. ($P_2$) represents the accuracy in the total area predicted by the models. The run time to analyze a full image (including cropping, detection, and analysis) was measured in an NVIDIA RTX 1000 Ada Gen. with 6 GB of VRAM. Concerning the relative errors, ($F_{re}$) refers to the foreground percentage, ($N_{re}$) to the number of objects detected, ($D_{re}$) and ($D_{m, re}$) to the average major and minor axes, respectively.

<table><thead><tr><th rowspan="2">Backbone</th><th rowspan="2">Parameters (M)</th><th rowspan="2">FOV (nm)</th><th rowspan="2">Threshold</th><th rowspan="2">Run time (s)</th><th colspan="4">Relative errors (%)</th><th rowspan="2">AP (%)</th><th rowspan="2">P<sub>1</sub></th><th rowspan="2">P<sub>2</sub></th></tr><tr><th>F<sub>re</sub></th><th>N<sub>re</sub></th><th>D<sub>re</sub></th><th>D<sub>m, re</sub></th></tr></thead><tbody><tr><td rowspan="3">ResNeXt-101</td><td rowspan="3">89.46</td><td>200</td><td>0.8</td><td>11.0</td><td>1.01</td><td>-3.68</td><td>-10.03</td><td>-3.54</td><td>25.0</td><td>95.1</td><td>83.6</td></tr><tr><td>400</td><td>0.7</td><td>3.0</td><td>-8.10</td><td>-0.26</td><td>-22.81</td><td>-21.33</td><td>33.0</td><td>94.4</td><td>60.6</td></tr><tr><td>533</td><td>0.6</td><td>1.9</td><td>-9.88</td><td>2.59</td><td>-30.13</td><td>-30.57</td><td>36.6</td><td>88.0</td><td>49.8</td></tr><tr><td rowspan="3">ResNet-50</td><td rowspan="3">26.58</td><td>200</td><td>0.9</td><td>6.6</td><td>0.10</td><td>-0.15</td><td>-13.27</td><td>-6.01</td><td>19.6</td><td>97.2</td><td>81.4</td></tr><tr><td>400</td><td>0.8</td><td>1.9</td><td>0.94</td><td>-4.07</td><td>-15.71</td><td>-12.54</td><td>28.1</td><td>93.9</td><td>70.7</td></tr><tr><td>533</td><td>0.7</td><td>1.2</td><td>2.37</td><td>-1.77</td><td>-20.33</td><td>-19.20</td><td>33.0</td><td>92.9</td><td>63.2</td></tr><tr><td rowspan="3">ResNet-18</td><td rowspan="3">13.77</td><td>200</td><td>0.9</td><td>4.7</td><td>-19.79</td><td>-6.48</td><td>-13.71</td><td>-3.99</td><td>28.2</td><td>89.8</td><td>77.5</td></tr><tr><td>400</td><td>0.8</td><td>1.4</td><td>-16.62</td><td>-18.54</td><td>-14.32</td><td>-10.13</td><td>30.7</td><td>85.4</td><td>62.7</td></tr><tr><td>533</td><td>0.7</td><td>0.9</td><td>-13.02</td><td>-11.74</td><td>-21.09</td><td>-19.92</td><td>33.2</td><td>82.1</td><td>55.8</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The top region of the testing frame (316L, 45.9 min) - each of the four crops is 400 nm wide. The ground truth segmentation is shown in red (a–d); predictions from a Mask R-CNN with ResNeXt-101 (32x4) as backbone using a FOV of 200 nm (e–h) and 400 nm (i–l). The white dotted box in the image (a) highlights a region in which the confidence of the segmentation is poor; the same happens on the line defects depicted in the image (d).</figcaption></figure>

thresholds are small, the average particle size decreases even further. For that reason, despite being a viable strategy, the distribution of loop sizes is negatively affected when using lower thresholds. Such behavior is evident in the ResNeXt-101 results from Table 1, when errors in the average major axis (D) go from -10.03 to -22.81% while increasing the FOV from 200 to 400 nm and reducing the threshold from 0.8 to 0.7.

It is worth providing a direct comparison between ResNeXt-101 and ResNet-50 (even though the thresholds for ResNet-50 are a little bit higher for the same FOV). While the predictions made by these two models are rather similar (please compare Figs. 3 and 4), ResNeXt-101 performs better in a 200 nm field of view ($P_2$ = 83.6%). Conversely, ResNet-50 performs better in larger FOVs. This is evident in Fig. 4h, for example, and it is reflected in $P_2$, as the total area of defects is less impacted in ResNeXt-50 than in ResNeXt-101 (with a 400 nm FOV, ResNet50's $P_2$ = 70.7% while ResNeXt's = 60.6%). ResNeXt-101 is more sensitive to increasing FOV, as it detects a larger number of small particles, including those in low-confidence regions, which are often poorly resolved and thus underestimated in size, leading to a

significant loss in both $F_{re}$ and $D_{re}$. In contrast, ResNet-50 tends to miss these small particles, resulting in a lower $N_{re}$. However, because such particles have a limited impact on the performance indexes, the overall metrics (especially $P_2$) remain more stable and superior to those of ResNeXt-101. In other words, ResNeXt-101, due to its grouped convolution design (higher cardinality), is better at picking up subtle features – including small, noisy ones –, which makes it more sensitive but also more prone to error when resolution is low. ResNet-50 is more conservative, missing small details but yielding more stable and robust performance on high-level metrics.

Finally, it is interesting to note that the ResNet-18 models cannot keep up their performance with larger models. At larger fields of view, the considerable foreground error indicates that many of the detected particles are duplicates detected in the same zones, overlapping each other. At last, ResNet-18 models are not suited for high-quality segmentation. Nevertheless, the segmentation obtained with ResNet-18 was included in the Supplementary Information, for completeness.

**External validation (ES1):** We will now explore how the selected models contribute to the interpretation of a second video about an
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** The top region of the testing frame (316L, 45.9 min) - each of the four crops is 400 nm wide. Predictions from a Mask R-CNN with ResNet-50 as backbone using a FOV of 200 nm (a–d) and 400 nm (e–h). The crops selected can be directly compared to Fig. 3.</figcaption></figure>

in situ experiment involving the 16Cr-37Fe-13Mn-34Ni (at. %) alloy, which will be designated insofar as the ES1 alloy. The experimental setup was identical to the one from the 316L irradiation experiments, with minor changes in the irradiation conditions: the temperature was set to 500 °C, and the irradiation time (and so the fluence) is divided by two. We will focus on predictions using a field of view of 200 nm. Similarly to the original (316L) video frames, images from the ES1 video consist of an integration of 13 frames acquired at each second of the video. However, unlike the training and testing dataset, we included a pre-processing step for the ES1 frames consisting of a Contrast Limited Adaptive Histogram Equalization (CLAHE) as implemented in OpenCV (Bradski, 2000). One of the main challenges of segmenting conventional TEM images is that it is hard to separate features of interest from the background when the signal-to-noise ratio is lower. CLAHE helps ensuring the full range of grayscale values available in 8-bit images are utilized, resulting in consistent illumination conditions across the video.

**Fig. 5** depicts the segmentation of four frames of the ES1 video predicted by ResNeXt-101 (upper row) and ResNet-50 (lower row). The segmentations are typically similar for the images with higher contrast on the right-hand side. Nevertheless, the model with ResNeXt-101 detects the early stages of loop formation (images on the left-hand side) better. This advantage is again highlighted in the size distributions shown in Fig. 6, obtained from the frames recorded at timestamps of 2 min, 2 min 30 s, 3 min 30 s, and 5 min 30 (the same as in Fig. 5). When comparing the distributions, their shape is very similar, both models capture the same trend on loop growth with time, but the distributions obtained with ResNeXt-101 from the 2 min frame is broader and have more counts. In any case, ResNet-50 also captures the size distributions well and could be used in cases in which faster predictions are necessary. As mentioned in Table 1, the time to segment a full 4K image in a consumer-grade GPU is around 11 s using the larger model but drops to 6.6 s when using ResNet-50.

Interestingly, the models can segment regions with a higher loop density than the training dataset. When we compare the distributions in Fig. 6 with the ones from the training dataset in Fig. 2, we can see a different behavior from both materials. In 316L, the simultaneous nucleation of new loops and very slow growth/coalescence of loops result in distributions in which the median size slowly changes towards larger values (unimodal to bimodal). On the other hand, in the ES1 alloy, the whole population of loops shifts to larger sizes, and the nucleation of new loops is less important to characterize the distribution.

**ES1 video analysis:** K.C. Russell and R.W. Powell (Russell and Powell, 1973) provide a basis for evaluating the nucleation and growth rates of dislocation loops in irradiated materials. After a rapid increase

in defect saturation from external irradiation, nucleation starts after a specific interval called the incubation time ($\tau$), which is usually a few seconds in pure metals. Once the incubation period is over, prismatic loops begin to form steadily, while small interstitial loops grow at a constant speed due to their elastic fields attracting more interstitials (bias effect). All loops will grow until they merge with others or the dislocation network (Derlet and Dudarev, 2020; Goryaeva et al., 2023). Eventually, the number of loops will reach a maximum value, determined by the materials properties or external conditions, such as temperature, irradiation flux, etc. Although these steps (incubation, nucleation, and growth) roughly describe the dislocation loop evolution in metals and alloys, it is worth reminding that such premises are insufficient to account for all phenomena in an irradiation experiment. For instance, at high temperatures, the thermal emission of vacancies from extended defects and the escape of interstitials to the foil surface cannot be neglected (Kiritani and Takata, 1978). These might cause the annihilation of loops at specific regions and explain the absence of dislocation loops when irradiation proceeds at temperatures above a given threshold (Veshchunov, 2023).

The digested data after segmentation of the full ES1 video is summarized in Fig. 7. It displays two box-plots showing loops' areal density and sizes (the average between major and minor axes) over time, according to the segmentation obtained with the larger model. As the in situ experiment progresses, we observe an incubation time of approximately 1 min 40 s, in which the number of loops does not change. The incubation time is likely shorter because the model only detects loops larger than approximately 2 nm in diameter with high confidence. The density of the dislocation loops increases rapidly after 1 min 40 s, reaching values close to $1.1 \times 10^{15} \text{ m}^{-2}$. The increase is accompanied by an increase in the loop diameter from 6 to 11 nm (at around the 3 min mark). The average size continues to rise to around 14 nm, but counts and sizes reach a stationary stage after 6 min. It is crucial to remember that at these reduced loop sizes (around 10 nm), the distinction between open elliptical loops and black dots becomes ambiguous (Li et al., 2018), especially in the case of parallel TEM, in which the background is increased in comparison with on-axis STEM. Therefore, we can only ensure statistical significance while grouping all instances as a unique class. Furthermore, in short videos at 500–550 °C, it is impossible to observe a drop in loop counts (density) due to surface emission, annihilation, or coalescence at later stages of the video because the system has not yet reached this regime. For more information on the characterization of dislocation loops in FCC alloys, please refer to Xiu et al. 2021 (Xiu et al., 2021). Ultimately, the segmentation of one frame every ten seconds already provides insights into the loop dynamics in the ES1 alloy; the loops show a distinct
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** The crops (400 nm wide) shown here are extracted from the original ES1 video. Predictions from a Mask-CNN-FPN 3x with ResNet-101 (a–d) and ResNet-50 (e–h) are overlayed on the images. Each image is composed of four predictions with FOV = 200 nm. Time stamps are, from the left to the right, respectively: 2 min, 2 min 30 s, 3 min 30 s, 5 min 30 s.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** ES1 video: Size distributions of the frames displayed in Fig. 5. Mask-CNN-FPN 3x with ResNet-101 was used to obtain the distributions in the top image (a), while ResNet-50 was used for the distributions in the bottom image (b). Due to the input resolution, loops smaller than 2 nm in diameter are identified as noise and excluded from the data analyses.</figcaption></figure>

growth over time, which is well described by the segmentation shown in Fig. 5 and well characterized with the box-plots in Fig. 7.

**Pure Cr video analysis:** The experimental setup was similar to the other experiments but employed Ni⁺ instead of Fe⁺ ions; the temperature was set to 500 °C and the total irradiation time is 5 min. The video segmentation concerning the experiment with pure Cr differs

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** ES1 video: Areal density (a) and average size (b) box-plots compiled after segmentation of 1 frame every 10 s of the original video. The segmentation was obtained using the optimized ResNeXt-101 model with a FOV of 200 nm.</figcaption></figure>

from the previous ones in many aspects: (i) it concerns a BCC material, not FCC; (ii) due to the low resolution the video was acquired (1024 x 1024 px); and (iii), due to faster events, images from the Cr video consist of an integration of only 8 frames acquired during each second of the video (the remaining frames are discarded). Given that the full FOV is the same as in the other videos (1600 nm), with a 2 times upscale of the base frames, we can extract 16 crops (512 x 512 px) with a 400 nm FOV. Overall, when analyzing images at low magnification,
it is advisable to upscale and divide them into several patches with appropriate fields of view rather than simply applying the segmentation to the entire image. As demonstrated in the previous section, even with the presence of *cropping artifacts*, the overall accuracy and reliability of the segmentation largely remain intact. For the pixel density in our 400 nm FOV images, the model using ResNet-50 as the backbone presents a performance equivalent to a larger model and enables us to perform the analysis on the fly. The main difference is that the ResNeXt-101 model detects more black dots with sizes ranging from 2 to 6 nm (see Figure 12 in the Supplementary Information), but most of these are treated as noise in the posterior data analysis. Regardless of what is presented herein, the user should always choose the larger model if small features are essential.

In BCC metals, black dots and small loops with burgers vector $a/2\langle 111 \rangle$ (which also appear as ellipses) are often indistinguishable. Note that $a\langle 100 \rangle$ loops also formed in BCC Fe because of an elastic instability close to the alpha-gamma transformation (Dudarev et al., 2008); however, in Cr, at 550 °C (close to our temperature), only $a/2\langle 111 \rangle$ loops are reported (Cui et al., 2024). Frames from our videos are bright field images captured under a two-beam condition where $g = [110]$, similar to those presented by Shen et al. (2021). On the other hand, given that only a fraction of the loops are visible on these illumination conditions, the areal densities presented in Fig. 8 are smaller than the absolute densities measured if multiple $g$ vectors are observed (Jenkins, 1994).

Fig. 8 compiles the data acquired during the pure Cr experiment. There are similarities between Cr and the ES1 alloy — for example, in pure Cr, the incubation time is shorter, around 25 s (0.42 min). In the following minute, the areal density of loops increases up to $9 \times 10^{14} \text{ m}^{-2}$, which is slightly lower than in the ES1 alloy and seems to remain stable after 2 min. From image 8b, the sizes in the incubation period cannot be trusted, given that artifacts are present in the field of view. It is nevertheless tricky to see any significant change in the median diameter, which starts near 8 nm and, at the end of the video, is still near 9 nm. When plotting only data on the upper quadrant of each timeframe (Fig. 8c), it is evident that larger particles are becoming larger, starting from 12.4 nm and growing to approximately 14 nm. The same trend is observed for the edges of the distribution, as indicated by the red arrow.

It is worth mentioning that this paper focuses on the segmentation workflow and brief data analysis. The data obtained with the proposed workflow is very complex and Figs. 6–8 are simply examples of which kind of analysis is promptly available upon segmentation. In pure Cr, black dots form after a short incubation time, and the burst stage is very marked; however, size variations are hard to characterize. In the ES1 alloy, the incubation time is higher than in pure Cr, the bursting stage is similar, and the growth of black dots and loops happens continuously over 5 min, lagging behind the bursting stage. At last, the behavior of 316L, from the reference dataset, shows a slow increase in areal density (compared to the marked burst in the other two videos), and a relatively low loop density at the end of the experiment. Additional data on the performance of the base models in both the ES1 and pure Cr videos can be found in Table 2 in the Supplementary Information.

## 4. Conclusions

This work briefly analyzes Mask R-CNN's capabilities for high-throughput data analysis of videos recorded during in situ TEM irradiation experiments. The proposed framework produced high-quality segmentation models that shed light on the early stages of dislocation loop formation (bursting stage), where changes in the microstructure occur rapidly. The models can also quickly identify the loop density saturation point and fully characterize size distributions over time. Our main findings are:

<figure><img src="image_10.png" /><figcaption>Fig. 8. Pure Cr video: Areal density (a) and average size (b) box plots compiled after segmentation of 1 frame every 2 s of the pure Cr video. The red box in (b) indicates the incubation stage, in which artifacts influence size. A detail of the diameters of the upper quadrant (Q3) is displayed in (c) to evidence the growth of this group of objects over time. The segmentation was obtained using the optimized ResNet-50 model with a FOV of 400 nm.</figcaption></figure>

1. Mask R-CNN performs well, segmenting small loops in relatively low-magnification videos, especially when provided with a capable enough backbone. ResNeXt-101 with 200 nm field of view (FOV) patches should be used in case small loops (2–6 nm) dominate the microstructure; for larger FOVs or later stages of loop dynamics, ResNet-50 presents equivalent segmentations with faster predictions. Based on our analysis, using ResNet-18 is not encouraged, given its poor metrics and faulted segmentation in defect-dense zones.

2. Predictions with our larger model (ResNeXt-101 backbone) take approximately 11 s per 4K frame on a conventional consumer-grade GPU (180 ms per image crop). Alternatively, by employing a smaller model (ResNet-50 backbone), our framework can pre-process frames, perform segmentation, and provide live updates of areal density and size variations across the (whole) 1600 nm field of view with a temporal resolution of 2 s (120 ms per image crop). If needed, inference speed can be further improved by reducing the number of image crops analyzed (e.g., analyzing only half of the original frame), increasing the crops' FOV, or lowering the input resolution. It is ultimately up to the
users to define the boundaries of their desired quality-framerate trade-off.

3. When comparing properties extracted from two videos, it is advisable to use models with similar parameters (FOV and/or threshold) to ensure consistent segmentation. Thresholds lower than 0.7 should be avoided. Even with these precautions, it is important to expect variations of around 10% in the median size of loops or the density of dislocation loops. Such variations can also occur due to movements of the transmission electron microscope (TEM) stage, focus, and other factors. Despite these inherent variations, we demonstrate that the framework can effectively contrast materials that exhibit dissimilar behaviors under irradiation.

In conclusion, the proposed framework may help researchers extract critical data on the dislocation loop dynamics from in situ or in operando experiments at different temperatures, even in the case of low-magnification or low-resolution videos. This approach also enables faster material qualification when developing novel radiation damage-resistant materials. A GitHub repository with a quick guide on analyzing in situ TEM videos using the models proposed here is provided for the community.

## CRediT authorship contribution statement

**Camilo A.F. Salvador:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. **Thomas Bilyk:** Writing – review & editing, Software, Methodology, Investigation. **Antoine Dartois:** Investigation, Data curation. **Lisa Vessier-Alary:** Investigation, Data curation. **Estelle Meslin:** Writing – review & editing, Supervision, Investigation, Conceptualization. **Mihai-Cosmin Marinica:** Writing – review & editing, Supervision, Methodology, Investigation, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

While preparing this work, the author(s) used *Grammarly* to check and fix syntax or grammar errors. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the publication's content.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work has been carried out within the Cross-Disciplinary Program on Numerical Simulation of the French Alternative Energies and Atomic Energy Commission (CEA) framework. The authors acknowledge the support from the JANNuS Orsay platform for the irradiation of the samples. C.A.F.S., T.B., M.C.M. acknowledge the support from GENCI - (Jean-Zay/CINES/CCRT) computer center under Grant No. A0190906973. We thank Aperam Stainless France for the manufacturing of the 316L alloy, and the Ecole des Mines de Saint-Etienne (EMSE) for providing the ES1 alloy.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.micron.2025.103927.

## Data availability

Detectron2 by Wu et al. (2019) is released under the Apache 2.0 license. The Python module used to prepare the dataset, train the model, perform predictions, and perform data analysis is available at https://github.com/ai-atoms/creme. All data necessary to reproduce the results of the published article are available, at the same address, after the review process.

## References

Allan, D., Caswell, T.A., Keim, N., Boulogne, F., Perry, R.W., Uieda, L., 2014. trackpy: Trackpy v0.2.4. Zenodo, http://dx.doi.org/10.5281/zenodo.12255.

Alloyeau, D., Pena, N.O., Wang, G., Ricolleau, C., Nelayah, J., 2025. In situ and operando transmission electron microscopy for the synthesis, fundamental understanding and catalytic applications of nanoalloys. Adv. Phys.: X 10 (1), 2481277. http://dx.doi.org/10.1080/23746149.2025.2481277.

Arakawa, K., Ono, K., Isshiki, M., Mimura, K., Uchikoshi, M., Mori, H., 2007. Observation of the one-dimensional diffusion of nanometer-sized dislocation loops. Science 318 (5852), 956–959. http://dx.doi.org/10.1126/science.1145386, Research Support, Non-U.S. Gov't.

Barr, C.M., Chen, E.Y., Nathaniel, J.E., Lu, P., Adams, D.P., Dingreville, R., Boyce, B.L., Hattar, K., Medlin, D.L., 2022. Irradiation-induced grain boundary facet motion: In situ observations and atomic-scale mechanisms. Sci. Adv. 8 (23), eabn0900. http://dx.doi.org/10.1126/sciadv.abn0900, URL https://www.science.org/doi/10.1126/sciadv.abn0900, Publisher: American Association for the Advancement of Science.

Bawane, K., Kamboj, A., Jin, M., Minaruzzaman, M., Alshannaq, M., Rickert, K., Mann, J.M., Teng, F., Childs, M., Shao, L., Hurley, D.H., Zhang, Y., Khafizov, M., Kombaiah, B., 2024. *In-Situ* TEM study of microstructural evolution in proton irradiated single crystal UO2 under high-temperature annealing. Acta Mater. 281, 120440. http://dx.doi.org/10.1016/j.actamat.2024.120440, URL https://www.sciencedirect.com/science/article/pii/S1359645424007900.

Bilyk, T., Goryaeva, A., Marinica, M.-C., Flament, C., Sabathier, C., Leroy, E., Loyer-Prost, M., Meslin, E., 2024. Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis. Sci. Rep. 14 (1), 25168. http://dx.doi.org/10.1038/s41598-024-74894-4.

Bradski, G., 2000. The OpenCV Library. Dr. Dobb's J. Softw. Tools.

Bruno, G., Lynch, M.J., Jacobs, R., Morgan, D.D., Field, K.G., 2023. Evaluation of human-bias in labeling of ambiguous features in electron microscopy machine learning models. Microsc. Microanal. 29 (Supplement_1), 1493–1494. http://dx.doi.org/10.1093/micmic/ozad067.767.

Burns, K., Tadj, K., Allaparti, T., Arias, L., Li, N., Aitkaliyeva, A., Misra, A., Scott, M.C., Hattar, K., 2024. Deep learning-enabled probing of irradiation-induced defects in time-series micrographs. APL Mach. Learn. 2 (1), 016117. http://dx.doi.org/10.1063/5.0186046, arXiv:https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0186046/19717395/016117_1_5.0186046.pdf.

Chen, Q., Zheng, C., Cui, Y., Lin, Y.-R., Zinkle, S.J., 2023. A deep learning model for automatic analysis of cavities in irradiated materials. Comput. Mater. Sci. 221, 112073. http://dx.doi.org/10.1016/j.commatsci.2023.112073, URL https://www.sciencedirect.com/science/article/pii/S0927025623000678.

Crocombette, J.-P., Wambeke, C.V., 2019. Quick calculation of damage for ion irradiation: implementation in Iradina and comparisons to SRIM. EPJ Nucl. Sci. Technol. 5, 7. http://dx.doi.org/10.1051/epjn/2019003, URL https://www.epj-n.org/articles/epjn/abs/2019/01/epjn190004/epjn190004.html, Publisher: EDP Sciences.

Cui, L., Du, Y., Yang, H., Schäublin, R.E., Zongda, Y., Kano, S., Hu, X., Abe, H., 2024. Nested loops explain low irradiation-induced swelling rate. Acta Mater. 267, 119700. http://dx.doi.org/10.1016/j.actamat.2024.119700, URL https://www.sciencedirect.com/science/article/pii/S1359645424000533.

Derlet, P.M., Dudarev, S.L., 2020. Microscopic structure of a heavily irradiated material. Phys. Rev. Mater. 4 (2), 023605. http://dx.doi.org/10.1103/PhysRevMaterials.4.023605, URL https://link.aps.org/doi/10.1103/PhysRevMaterials.4.023605.

Dudarev, S.L., Bullough, R., Derlet, P.M., 2008. Effect of the α-γ phase transition on the stability of dislocation loops in bcc iron. Phys. Rev. Lett. 100 (13), 135503. http://dx.doi.org/10.1103/PhysRevLett.100.135503, URL https://link.aps.org/doi/10.1103/PhysRevLett.100.135503, Publisher: American Physical Society.

Foley, D.L., Mang, E.H., Wang, Y., Spearot, D.E., Taheri, M.L., 2025. Low frequency band pass Fourier filtering for irradiation damage analysis in the transmission electron microscope. Micron 196–197, 103864. http://dx.doi.org/10.1016/j.micron.2025.103864, URL https://www.sciencedirect.com/science/article/pii/S0968432825000824.

Gentils, A., Cabet, C., 2019. Investigating radiation damage in nuclear energy materials using jannus multiple ion beams. Nucl. Instruments Methods Phys. Res. Sect. B: Beam Interactions Mater. Atoms 447, 107–112. http://dx.doi.org/10.1016/j.nimb.2019.03.039, URL https://www.sciencedirect.com/science/article/pii/S0168583X19301697.
Girshick, R., Donahue, J., Darrell, T., Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 580–587. http://dx.doi.org/10.1109/CVPR.2014.81, URL https://ieeexplore.ieee.org/document/6909475.

Glorot, X., Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, pp. 249–256, URL https://proceedings.mlr.press/v9/glorot10a.html.

Goryaeva, A.M., Domain, C., Chartier, A., Dézaphie, A., Swinburne, M.D.T., Ma, K., Loyer-Prost, M., Creuze, J., Marinica, M.-C., 2023. Compact A15 Frank-Kasper nano-phases at the origin of dislocation loops in face-centred cubic metals. Nat. Commun. 14 (1), 3003. http://dx.doi.org/10.1038/s41467-023-38729-6, URL https://www.nature.com/articles/s41467-023-38729-6.

Govind, K., Oliveros, D., Dlouhy, A., Legros, M., Sandfeld, S., 2024. Deep learning of crystalline defects from TEM images: a solution for the problem of 'never enough training data'. Mach. Learn.: Sci. Technol. 5 (1), 015006. http://dx.doi.org/10.1088/2632-2153/ad1a4e.

Groom, D.J., Yu, K., Rasouli, S., Polarinakis, J., Bovik, A.C., Ferreira, P.J., 2018. Automatic segmentation of inorganic nanoparticles in BF TEM micrographs. Ultramicroscopy 194, 25–34. http://dx.doi.org/10.1016/j.ultramic.2018.06.002, URL https://www.sciencedirect.com/science/article/pii/S0304399117302012.

He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017. Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision. ICCV, pp. 2980–2988. http://dx.doi.org/10.1109/ICCV.2017.322.

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 770–778. http://dx.doi.org/10.1109/CVPR.2016.90.

Horwath, J.P., Zakharov, D.N., Mégret, R., Stach, E.A., 2020. Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images. Npj Comput. Mater. 6 (1), 108. http://dx.doi.org/10.1038/s41524-020-00363-x.

Hwang, S., Chen, X., Zhou, G., Su, D., 2020. In situ transmission electron microscopy on energy-related catalysis. Adv. Energy Mater. 10 (11), 1902105. http://dx.doi.org/10.1002/aenm.201902105, URL https://onlinelibrary.wiley.com/doi/abs/10.1002/aenm.201902105, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aenm.201902105.

Inkscape Project, 2024. Inkscape. URL https://inkscape.org.

Ioffe, S., Szegedy, C., 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. ICML '15, JMLR.org, Lille, France, pp. 448–456.

Jacobs, R., 2022. Deep learning object detection in materials science: Current state and future directions. Comput. Mater. Sci. 211, 111527. http://dx.doi.org/10.1016/j.commatsci.2022.111527, URL https://www.sciencedirect.com/science/article/pii/S0927025622002804.

Jacobs, R., Shen, M., Liu, Y., Hao, W., Li, X., He, R., Greaves, J.R., Wang, D., Xie, Z., Huang, Z., Wang, C., Field, K.G., Morgan, D., 2022. Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs. Cell Rep. Phys. Sci. 3 (5), 100876. http://dx.doi.org/10.1016/j.xcrp.2022.100876, URL https://www.sciencedirect.com/science/article/pii/S2666386422001503.

Jenkins, M., 1994. Characterisation of radiation-damage microstructures by TEM. J. Nucl. Mater. 216, 124–156. http://dx.doi.org/10.1016/0022-3115(94)90010-8, URL https://www.sciencedirect.com/science/article/pii/0022311594900108.

Kiritani, M., Takata, H., 1978. Dynamic studies of defect mobility using high voltage electron microscopy. J. Nucl. Mater. 69–70, 277–309. http://dx.doi.org/10.1016/0022-3115(78)90249-0, URL https://www.sciencedirect.com/science/article/pii/0022311578902490.

Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems. 25, Curran Associates, Inc., pp. 1–9, URL https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c436e924a68c45b-Paper.pdf.

Li, W., Field, K.G., Morgan, D., 2018. Automated defect analysis in electron microscopic images. Npj Comput. Mater. 4 (1), 36. http://dx.doi.org/10.1038/s41524-018-0093-8.

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014. Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds.), Computer Vision - ECCV 2014. Springer International Publishing, Cham, pp. 740–755. http://dx.doi.org/10.1007/978-3-319-10602-1-48.

Lin, R., Zhang, R., Wang, C., Yang, X.-Q., Xin, H.L., 2021. TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images. Sci. Rep. 11 (1), 5386. http://dx.doi.org/10.1038/s41598-021-84499-w.

Liu, Y.-C., Wu, H., Mayeshiba, T., Afflerbach, B., Jacobs, R., Perry, J., George, J., Cordell, J., Xia, J., Yuan, H., Lorenson, A., Wu, H., Parker, M., Doshi, F., Politowicz, A., Xiao, L., Morgan, D., Wells, P., Almirall, N., Yamamoto, T., Odette, G.R., 2022. Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels. Npj Comput. Mater. 8 (1), 85. http://dx.doi.org/10.1038/s41524-022-00760-4.

Lu, C., Chen, K., Qiu, H., Chen, X., Chen, G., Qi, X., Jiang, H., 2024. Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy. Nat. Commun. 15 (1), 4677. http://dx.doi.org/10.1038/s41467-024-49125-z.

Moncomble, A., Alloyeau, D., Moreaud, M., Khelfa, A., Wang, G., Ortiz-Pena, N., Amara, H., Gatti, R., Moreau, R., Ricolleau, C., Nelayah, J., 2025. Aquadenoising: AI-enhancement of in situ liquid phase STEM video for automated quantification of nanoparticles growth. Ultramicroscopy 271, 114121. http://dx.doi.org/10.1016/j.ultramic.2025.114121, URL https://www.sciencedirect.com/science/article/pii/S0304399125000208.

Parrish, R.J., Buffard, D.C., Frazer, D.M., Taylor, C.A., Gutierrez-Kolar, J., Buller, D.L., Boyce, B.L., Hattar, K., 2021. Exploring coupled extreme environments via in-situ transmission electron microscopy. Microsc. Today 29 (1), 28–34. http://dx.doi.org/10.1017/S1551929520001595, arXiv:https://academic.oup.com/mt/article-pdf/29/1/28/47464258/mt0028.pdf.

Rahman, M.M., Chen, W.-Y., Mu, L., Xu, Z., Xiao, Z., Li, M., Bai, X.-M., Lin, F., 2020. Defect and structural evolution under high-energy ion irradiation informs battery materials design for extreme environments. Nat. Commun. 11 (1), 4548. http://dx.doi.org/10.1038/s41467-020-18345-4, URL https://www.nature.com/articles/s41467-020-18345-4, Publisher: Nature Publishing Group.

Redmon, J., Farhadi, A., 2018. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.), Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015, Springer International Publishing, Cham, pp. 234–241. http://dx.doi.org/10.1007/978-3-319-24574-4_28.

Russell, K., Powell, R., 1973. Dislocation loop nucleation in irradiated metals. Acta Metall. 21 (3), 187–193. http://dx.doi.org/10.1016/0001-6160(73)90002-3, URL https://www.sciencedirect.com/science/article/pii/0001616073900023.

Saaim, K.M., Afriidi, S.K., Nísar, M., Islam, S., 2022. In search of best automated model: Explaining nanoparticle TEM image segmentation. Ultramicroscopy 233, 113437. http://dx.doi.org/10.1016/j.ultramic.2021.113437, URL https://www.sciencedirect.com/science/article/pii/S0304399121002138.

Shen, M., Li, G., Wu, D., Yaguchi, Y., Haley, J.C., Field, K.G., Morgan, D., 2021. A deep learning based automatic defect analysis framework for in-situ TEM ion irradiations. Comput. Mater. Sci. 197, 110560. http://dx.doi.org/10.1016/j.commatsci.2021.110560, URL https://www.sciencedirect.com/science/article/pii/S09270256210028071.

Taheri, M.L., Stach, E.A., Arslan, I., Crozier, P., Kabius, B.C., LaGrange, T., Minor, A.M., Takeda, S., Tanase, M., Wagner, J.B., Sharma, R., 2016. Current status and future directions for in situ transmission electron microscopy. Ultramicroscopy 170, 86–95. http://dx.doi.org/10.1016/j.ultramic.2016.08.007, URL https://www.sciencedirect.com/science/article/pii/S0304399116310280.

TorchVision maintainers and contributors. 2016. TorchVision: Pytorch's computer vision library. GitHub Repository https://github.com/pytorch/vision.

Veshchunov, M., 2023. On the theory of dislocation loop nucleation in irradiated crystals. J. Nucl. Mater. 577, 154310. http://dx.doi.org/10.1016/j.jnucmat.2023.154310, URL https://www.sciencedirect.com/science/article/pii/S0022311523000806.

Wang, C., Luo, H., Wang, J., Groom, D., 2024. DDDNet: A lightweight and robust deep learning model for accurate segmentation and analysis of TEM images. APL Mater. 12 (11), 111107. http://dx.doi.org/10.1063/5.0228023.

Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., Girshick, R., 2019. Detectron2. https://github.com/facebookresearch/detectron2.

Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K., 2017. Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 5987–5995. http://dx.doi.org/10.1109/CVPR.2017.634.

Xiu, P., Bei, H., Zhang, Y., Wang, L., Field, K.G., 2021. STEM characterization of dislocation loops in irradiated FCC alloys. J. Nucl. Mater. 544, 152658. http://dx.doi.org/10.1016/j.jnucmat.2020.152658, URL https://www.sciencedirect.com/science/article/pii/S0022311520312666.

Yvon, P. (Ed.), 2017. Structural Materials for Generation IV Nuclear Reactors. Woodhead Publishing, http://dx.doi.org/10.1016/C2014-0-03589-7.

Zavodov, A.V., 2021. Removal of illumination gradient in TEM images. Algorithm implementation in TEM imaging& analysis software. Crystallogr. Rep. 66 (6), 1156–1163. http://dx.doi.org/10.1134/S1063774521040234.

Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J., 2018. UNet++: A nested U-net architecture for medical image segmentation. In: Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.a.M.R., Bradley, A., Papa, J.a.P., Belagiannis, V., Nascimento, J.C., Lu, Z., Conjeti, S., Moradi, M., Greenspan, H., Madabhushi, A. (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing, Cham, pp. 3–11. http://dx.doi.org/10.1007/978-3-030-00889-5_1.