dataesr/scientific-paragraphs-categorization huggingface dataset:
The designed method is capable of segmenting myocardium borders and extracting multiscale features using an ASPP module for all segments in the complete cardiac cycle. Zheng et al. [34]  proposed a model to address the overfitting problem in deep CNN based on two-stage training methods such as: 1-pretraining and 2-implicit regularization training. In the first method, image representation is extracted through training a model for anomaly detection. In the second method, the model is retrained based on anomaly detection results to standardize the feature boundary and converge it in a suitable position. In another study, Zheng et al. [35]  proposed a full stage data augmentation framework to enhance the accuracy of deep convolutional neural networks. This model acts as an implicit ensemble model without additional training costs.
Dataset A dataset from 56 subjects with SAX MR sequences based on a clinical environment was used for model training and validation. Each subject consisted of 20 frames and the pixel layout of the MR images was assorted between 0. 6836 mm/pixel and 2. 0833 mm/pixel, in addition to a mode with 1. 5625 mm/pixel. The study obtained the dataset from three medical resources associated with health care hubs, namely, London and St. Joseph Healthcare Centers based on scanners of two merchants (GE & Siemens)  [45, 46]. The dataset is publicly available  [45]  with subjects having ages from 16 years to 97 years and averaging 58. 9 years. The ground truth for each frame for all subjects was provided for endocardium and epicardium borders. The individual subject data comprised 20 frames all over the cardiac cycle.
Preprocessing of the Dataset A dynamic histogram equalization technique was used for contrast enhancement of input cardiac MRI images. The raw and contrast images are shown in Figure  1  for different numbers of patients. The input slices with enhanced slices and ground truth mask are shown in Figure  1. was used for model training and validation. Each subject consisted of 20 frames and pixel layout of the MR images was assorted between 0. 6836 mm/pixel and 2. mm/pixel, in addition to a mode with 1. 5625 mm/pixel. The study obtained the da from three medical resources associated with health care hubs, namely, London an Joseph Healthcare Centers based on scanners of two merchants (GE & Siemens)  [45  The dataset is publicly available  [45]  with subjects having ages from 16 years to 97 y and averaging 58.
Proposed Model The proposed model is a residual network comprising an encode-decode network. The ASPP module is integrated at the bottom of the proposed model. Figure  2  depicts the block diagram of the proposed model for myocardium segmentation using MR cardiac images. The input feature maps from the encoder side are passed to the ASPP module, and output feature maps after the ASPP module are provided to the decoder part of the proposed model. Details of each module are explained in the following sections. Mathematics 2022, 10, 627 6 of 23.
ASPP Module The ASPP network is a combination of various parallel atrous convolutions with different rates, global average pooling and a 1 × 1 convolutional layer. The proposed network can capture contextual information at multiple scales using atrous convolution with different filter kernels. In addition, it can extract multiscale features from the input feature maps for myocardium border segmentation. Atrous convolution is a promising tool for capturing multiscale feature information by controlling different resolutions by adjusting.
ASPP Module The ASPP network is a combination of various parallel atrous convolutions with different rates, global average pooling and a 1 × 1 convolutional layer. The proposed network can capture contextual information at multiple scales using atrous convolution with different filter kernels. In addition, it can extract multiscale features from the input feature maps for myocardium border segmentation. Atrous convolution is a promising tool for capturing multiscale feature information by controlling different resolutions by adjusting different receptive fields  [47]. Atrous convolution was applied on the input x for each pixel i on the output y and filter w as shown in Equation (1): y[i] = ∑ k x[i + r. k]w[k] (1) Here, the atrous rate r controls the stride for sampling the input image. The subjected convolution inserts r -1 zeros between two successive filter values and convolves the input x with those filters.
The receptive field of filter can be modified by adjusting the filter rate r. We integrated an ASPP module was used in DeepLabv3  [48]  in the proposed residual network to improve the segmentation results. ASPP consisted of four atrous convolutions layers using different atrous rate r and one global average pooling layer. Three parallel atrous convolution layers were used with 3 × 3 size rates of 3, 5 and 7, and one 1 × 1 convolution layer. The resulting features from the four convolutions and one overall pooling average layer were up sampled bilinearly to the feed size and then concatenated and provided to another 1 × 1 type convolution. The ASPP was employed to the feature map produced by the bottom of the encoder segment and the outcome feature map from ASPP were provided to the decoder part of the network.
Most of the samples shows more than 96 percent agreement between predicted and ground truth values for all proposed models. Figure  7h  shows that the ensemble model produced 99 percent histogram agreement for most of the samples. This pixel agreement analysis shows that the proposed model produced reliable and accurate segmentation results using cardiac MRI-validated dataset. Pixel agreement analysis revealed that our proposed model could be used for myocardium border segmentation. Bland Altman analysis was conducted using ground truth and predicted segmentation values for all frames of the test data. Figure  8  shows the Bland Altman analysis for the chosen best models (proposed and existing). The analysis showed a 95% agreement between predicted and ground truth samples for all test data.
Visualization Segmentation Results The proposed model and existing deep learning models were used to evaluate segmentation of the myocardium border using heart MRI dataset. An 80% dataset was used for training and 20% for testing. The actual and segmented contours are shown in Figure  10. Table  3  shows comparison of the proposed ensemble model with datasets. Alain et al. [57]  proposed a dataset based on left ventricle myocardium segmentation. The proposed model also evaluated a multi-sequence cardiac MRI dataset  [58]. The dataset used in this paper with other publicly available datasets are shown in Table  3. Bland Altman analysis was conducted using ground truth and predicted segmentation values for all frames of the test data. Figure  8  shows the Bland Altman analysis for the chosen best models (proposed and existing).
Visualization Segmentation Results The proposed model and existing deep learning models were used to evaluate segmentation of the myocardium border using heart MRI dataset. An 80% dataset was used for training and 20% for testing. The actual and segmented contours are shown in Figure  10. Table  3  shows comparison of the proposed ensemble model with datasets. Alain et al. [57]  proposed a dataset based on left ventricle myocardium segmentation. The proposed model also evaluated a multi-sequence cardiac MRI dataset  [58]. The dataset used in this paper with other publicly available datasets are shown in Table  3. Training and validation loss curves for the proposed model are shown in Figure  11. (a) Figure  10. Pixel agreement between actual and predicted masks for 20 frames of test patient data using the best proposed and existing models.
Visualization Segmentation Results The proposed model and existing deep learning models were used to evaluate segmentation of the myocardium border using heart MRI dataset. An 80% dataset was used for training and 20% for testing. The actual and segmented contours are shown in Figure  10. Table  3  shows comparison of the proposed ensemble model with datasets. Alain et al. [57]  proposed a dataset based on left ventricle myocardium segmentation. The proposed model also evaluated a multi-sequence cardiac MRI dataset  [58]. The dataset used in this paper with other publicly available datasets are shown in Table  3. The training and validation loss curves are shown in Figure  11. The loss curves did not provide smooth learning when we used the Liu dataset  [58]  and resulted in overfitting; the reason being the small number of samples available for training.
Discussion The performance of the proposed framework was evaluated using various performance metrics for semantic segmentation. Performance metrics such as Dice coefficients, Jaccard coefficients, volume over error, Housdrouf distance, maximum average surface distance, accuracy, sensitivity, specificity were used to assess the accuracy between actual The training and validation loss curves are shown in Figure  11. The loss curves did not provide smooth learning when we used the Liu dataset  [58]  and resulted in overfitting; the reason being the small number of samples available for training. The dataset provided by Xue  [45]  produced better training and validation loss compared to the Liu dataset  [58]. Table  4  shows the number of trainable parameters and number of flops used during optimization and training of proposed model with different number of hyperparameters.
Discussion The performance of the proposed framework was evaluated using various performance metrics for semantic segmentation. Performance metrics such as Dice coefficients, Jaccard coefficients, volume over error, Housdrouf distance, maximum average surface distance, accuracy, sensitivity, specificity were used to assess the accuracy between actual and predicted values based on each frame of the input dataset. Other performance metrics such as correlation coefficients, agreement between binary and Bland Altman plots between actual and predicted masks were assessed to determine the accuracy of the proposed model. Slice-by-slice popular segmentation deep learning models such as Unet, SegNet, FractelNet, ResNet, Attention U-net were reimplemented and results compared with our proposed Residual neural network including an atrous spatial pyramid pooling module deep learning model.
The proposed model4 segmented the full myocardium border, as shown in Figure  13. The ensemble model produced excellent results and segmented the full border with different shapes and sizes of the myocardium border. The atrous spatial pyramid pooling module is used successfully for segmentation of computer vision and biomedical image semantic segmentation problems. The ASPP module integrated with the proposed encoder-decoder residual network has the ability to extract detailed features and achieved good performance compared with recent deep learning models. Some boundary pixels of myocardium borders still require reconstruction using deep learning models. In the proposed ensemble solution we took maximum pixel values from the same location using different proposed models. Standard convolution usually uses a convolution kernel with a smaller receptive field, making it impossible to obtain contextual information.
Conclusions Quantification of the myocardium is an essential task for identification of myocardial infraction and other heart diseases. The main challenge is to segment the myocardium border that has different shapes and sizes of the myocardium border zone and the same texture as the surrounding tissues. Various deep learning models have been proposed to tackle the myocardium border segmentation task. In this paper, a model based on a residual encoder-decoder integrated with an ASPP module was presented for myocardium segmentation using a cardiac MRI dataset. Structural information for feature maps could be restored using the proposed ASPP with the addition of residual blocks and different hyperparameters used in training of various proposed residual block with ASPP module has various advantages compared to traditional CNN-based models.
The main advantages are (1) that the gradient can flow continuously, allowing the parameters to be updated in very deep networks, (2) the operations applied by a single layer are only a small modification to identity operation, and (3) ResNets modules are robust to layer permutations suggesting that neighboring layers perform similar operations. The main purpose of such residual blocks is the safeguarding of feature maps within convolutional layer blocks that are used before each encoder and decoder block required to bridge the semantic gap between the encoder and decoder by maintaining small increments in the computational overhead and providing an accurate segmentation map. The structural information for feature maps could be restored using ASPP with the addition of the residual blocks aimed to preserve the fine-grained structures that play an important role in medical image segmentation.
Conclusions Quantification of the myocardium is an essential task for identification of myocardial infraction and other heart diseases. The main challenge is to segment the myocardium border that has different shapes and sizes of the myocardium border zone and the same texture as the surrounding tissues. Various deep learning models have been proposed to tackle the myocardium border segmentation task. In this paper, a model based on a residual encoder-decoder integrated with an ASPP module was presented for myocardium segmentation using a cardiac MRI dataset. Structural information for feature maps could be restored using the proposed ASPP with the addition of residual blocks and different hyperparameters used in training of various models. We chose the best models based on the experimental results.
Further the ensemble method based on a majority voting technique was applied to fuse the segmentation output of the best proposed and existing deep learning models. The ASPP module was used to extract relatively local features and multi-scale features to provide rich context information for the proposed network. Further, the ASPP and residual blocks from the encoder and decoder side of the proposed model were used to reduce the semantic gap between the high-level feature and the low-level feature maps to improve feature fusion capability and improve the performance of the myocardium border segmentation task. The results show that the proposed deep learning model could be used for cardiac myocardium segmentation. Accurate segmentation and detection of myocardium borders could be helpful in diagnosing myocardial infraction, no reflow and hypertrophic cardiomyopathy (HCM) disease.
Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0. 793% recall and 0. 723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection.
, 2013; Rousseau et al. , 2015a; Meĺine et al. , 2019; Peŕez-Bueno et al. , 2019; Meĺine et al. , 2020; Valcke, 2021). CFI has potential use for monitoring the damaging effect of diseases on plants from the laboratory to the field scale  (Ivanov and Bernards, 2016). Coupled with semantic segmentation, CFI can be deployed to target location, size, and the disease state at an early stage of its progress  (Ampatzidis, 2018; Singh et al. , 2020). Moreover, automatic estimation of the diseased plant area can describe the disease's epidemiological characteristics, understand disease dynamics, assess its propagation speed, and thus facilitate management decisions. One needs a large annotated image dataset with labeled diseased plant tissues to produce a precise model for automatic disease segmentation.
The colossal effort of dataset annotation is ongoing, as highlighted in recent reviews on computer vision for scoring plant diseases  (Singh et al. , 2020; Abade et al. , 2021; Li et al. , 2021; Liu and Wang, 2021). Moreover, the international community in computer vision for plant pathology makes an additional effort by publicly sharing annotated datasets  (Lu and Young, 2020). Notice that most available annotated datasets of diseased plants are in standard RGB color imaging and for disease stages when symptoms differ from healthy tissues quite well, by color and contrast. Several challenges exist for disease annotation in CFI at the earlier stages, such as (a) detection by the eye of the disease spots in noisy monochromatic images; (b) extremely tiny size of disease lesions; (c) a large number of scattered lesions to annotate.
So far, to the best of our knowledge, only one annotated dataset of fluorescent images of diseased Arabidopsis thaliana plants is currently available  (Pavicic et al. , 2021). However, the automated annotation of Botrytis cinerea fungal disease was performed for disease severity exceeding 8%. Thus, the high human labor cost of manual annotation results in the lack of annotated fluorescence images of diseased plants suitable for disease segmentation  (Lu and Young, 2020). One way to circumvent this difficulty is to simulate a synthetic dataset with automated annotation. Using a synthetic dataset for the model training alleviates the annotation cost and augments the dataset, thus improving the model's ability to generalize  (Douarre et al. , 2019; Abbas et al. , 2021). This approach has been widely used in plant disease classification  (Sun et al.
, 2020; Abbas et al. , 2021; Cap et al. , 2022). In contrast, in disease detection and segmentation, there are still only a few examples of the synthetic datasets  (Douarre et al. , 2019; Zhang et al. , 2021). Additionally, since RGB cameras dominate plant disease monitoring  (Iqbal et al. , 2018) , having the more affordable cost  (Mavridou et al. , 2019) , image analysis methods are adapted for this type of imaging, which results in the creation of RGB synthetic datasets. However, RGB imaging is unsuitable for early disease diagnosis and is sensitive to illumination conditions that can significantly alter color-based segmentation accuracy  (Iqbal et al. , 2018; Mavridou et al. , 2019). To fill this gap, our goal was twofold: (1) to develop a novel methodology for generating fluorescent images of plants with an automated disease lesion annotation; (2) to illustrate the efficacy of the trained model on the synthetic dataset for segmentation lesions on an empirical CFI dataset of Arabidopsis thaliana infected by the bacterium Pseudomonas syringae pv.
tomato. This traditional way of transfer learning from synthetic images to real ones is called the sim2real transfer. The first generation of sim2real on fluorescence imaging was proposed in 2019 on healthy Arabidopsis thaliana plants for leaf segmentation  (Sapoukhina et al. , 2019). Here, we extended this approach to the case of disease lesion segmentation. First, we analyzed the principal statistics of the empirical CFI dataset to derive some relationships that fluorescent synthetic data should respect. Second, we created a synthetic dataset under derived conditions. Third, we trained the U-Net model on synthetic data to segment disease lesions and transferred the model to the empirical CFI dataset. Finally, we discussed the conditions for successful sim2real transfer.
The same approach was used by  (Pavicic et al. , 2021). The authors defined the pixel value threshold of F v / F m ≤0. 75 as the cutoff for symptomatic pixels of Arabidopsis leaflets infected with a Botrytis strain. Since dark adaptation for F v /F m measurements makes it difficult to translate to agricultural fields, other photosynthetic parameters and a combination of CFI with other imaging techniques have been studied for the detection of plant diseases  (Bauriegel and Herppich, 2014; Mutka and Bart, 2015; Ivanov and Bernards, 2016). Despite its limitations, the F v /F m measurement remains state-of-the-art for studying plantpathogen interactions under laboratory conditions. In this study, we exploit the F v /F m decay property to simulate diseased pixels on Arabidopsis plant images and thus create a synthetic dataset of fluorescent images of diseased plants.
Real-Fluo-Healthy and Real-Fluo-Diseased datasets described further were acquired with the PSI Open FluorCam FC 800-O (PSI, Brno, Czech Republic), capturing chlorophyll fluorescence images and estimating the maximum quantum yield of PSII, F v /F m , on rosettes of Arabidopsis thaliana ecotype Col0. The system sensor was a CCD camera with a pixel resolution of 512 by 512 and a 12-bit dynamic range. The system included 4 LED panels divided into two pairs. One pair provided an orange actinic light with a wavelength of around 618 nm, with an intensity varying from 200 to 400 mmol m -2 s -1. It provided a 2s pulse that allowed measuring the initial fluorescent state, F 0. The other pair provided a saturating pulse during 1s in blue wavelength, typically 455 nm, with an intensity of up to 3000 mmol m -2 s -1.
CVPPP dataset To design plant shapes in Synthetic-Fluo-Diseased dataset, we used the open dataset provided in the Leaf Segmentation Challenge held as part of the computer vision problems in plant phenotyping CVPPP workshop  (Minervini et al. , 2016). CVPPP dataset consists in 27 RGB images of tobacco plants and 783 RGB images of Arabidopsis wild and mutant plants. We considered only the Arabidopsis dataset in this study. All images are hand-labeled to obtain ground truth masks for each leaf in the scene, as described in  (Minervini et al. , 2015). These masks are image files encoded in PNG where each segmented leaf is identified with a unique integer value, starting from 1, where 0 is the background. We used plant labels of CVPPP dataset to produce binary masks of plant shapes on which further we added texture of Gaussian noise simulating CFI.
Real-Fluo-Healthy dataset To derive the principle measurements of CFI on healthy plants, we acquired 49 fluorescent images of Arabidopsis inoculated with sterile water (mock), named further as an empirical Real-Fluo-Healthy dataset. We emphasize that the dataset included only healthy plants. Plants were imaged in chlorophyll fluorescence for 12 days after inoculation to obtain F 0 , F m tiff images. We analyzed the distribution of the F 0 and F m parameters for Real-Fluo-Healthy images, deriving m F 0 , s F 0 , m F m , s F m at each developmental stage (Table  1 ). These values were used further to simulate fluorescent quantum efficiency, F v /F m =(F m -F 0 )/F m , of healthy pixels of plants.
Real-Fluo-Diseased dataset Four weeks after seedlings, Arabidopsis thaliana ecotype Col0 plants were inoculated with the bacterial Pseudomonas syringae pv. tomato strain DC3000 as described in Section 2. 2. There were 18 dishes with four Arabidopsis thaliana plants per dish, inoculated with DC3000 bacterial strain. Each dish was imaged in chlorophyll fluorescence every two days after inoculation to obtain F 0 , F m , and F v /F m tiff images. The dynamics of symptoms growth were followed till the eighth day after inoculation. It resulted in 108 images of size 512 x 512 containing four plants. The disease lesions were annotated with a thresholding approach based on the probability of misclassification of a healthy pixel into the class of diseased pixels  (Rousseau et al. , 2013). Thus, we created an empirical Real-Fluo-Diseased dataset used further to understand the effect of disease on the fluorescence parameter values.