Introduction介绍

Caenorhabditis elegans is a small free-living nematode widely used as a model organism in biological and genetic research due to its simple anatomy, short lifecycle, and well-annotated genome. Analyzing C. elegans images is crucial for studies on aging, development, and the effects of genetic mutations. Traditional image analysis methods are time-consuming and require human expertise. 秀丽隐杆线虫是一种小型自由生活的线虫，由于其解剖结构简单、生命周期短和基因组注释良好而被广泛用作生物和遗传研究中的模型生物。秀丽隐杆线虫图像的分析对于各种研究都至关重要，包括衰老、发育和基因突变的影响。传统的图像分析方法非常耗时，并且需要人类的专业知识。WormCNN addresses these challenges by offering an automated and efficient deep learning-based image analysis solution.通过提供自动化、高效的基于深度学习的图像分析解决方案来解决这些挑战。

WormCNN is是一个卷积神经网络 a convolutional neural network (CNN) model模型，用于对秀丽隐杆线虫进行年龄分类和回归分析^{[ 1 ]}。作者介绍了一种在 designed384 for age classification and regression analysis of C. elegans images . The authors introduce a method called 孔板中液体中单独培养线虫的方法 (SingleWorm, where individual worms are cultured in liquid in 384-well plates, and daily images of surviving worms are captured as a dataset.)，并以存活线虫的每日图像捕获作为数据集。

After training the model, the predicted biological age is compared with reference data based on lifespan indicators in liquid culture. This allows for calculating the 训练模型后，通过将预测的生物年龄与基于液体培养中的寿命指标的参考数据进行比较，计算出“Healthy Aging Index” (HAI), which quantitatively assesses the aging health of worms. The model also enables binary classification of worms as "young" or "old."健康衰老指数”（HAI），从而定量衡量线虫的衰老健康状况。该模型还允许将线虫二分类为“年轻”或“年老”。

WormCNN introduces a method for linearizing worm images.提出了一种线性化线虫图像的方法。 

Similar to tokenization in natural language processing, the 与自然语言处理中的标记化过程类似，WormCNN method divides worm images into multiple "tokens" based on the overall direction of the worm. These tokens are then recombined to create a linearized version of the worm image. This preprocessing step is crucial in reducing variations caused by distortions, bends, or other morphological changes commonly seen in worm movement. By linearizing the images, the model can focus on detecting biological feature changes without being distracted by pose differences. This standardization improves the overall accuracy of classification and regression tasks.方法根据线虫图像的整体方向将其划分为多个“标记”。然后重新组合这些标记以创建线虫图像的线性化版本。此预处理步骤在减少线虫运动中常见的扭曲、弯曲或其他形态变化引起的变化方面起着至关重要的作用。通过线性化图像，模型能够专注于检测生物特征变化，而不会被姿势差异分散注意力。这种标准化提高了分类和回归任务的整体准确性。

预处理步骤详情

Preprocessing Details

1. 图像捕获和数据收集：每天在受控环境中使用 SingleWorm 方法捕获线虫图像，其中线虫在 384 孔板中单独培养。这种方法最大限度地减少了线虫之间的相互作用，并确保收集的图像准确代表每个个体的形态状态，而不会受到其他蠕虫的干扰。图像通常以灰度捕获，这简化了处理并减少了计算负荷。

2. 骨架化和特征提取：在预处理过程中，每条线虫的图像都会转换为骨架结构。此过程涉及检测线虫的主体并将其简化为骨架线，保留身体曲线等关键结构特征，同时丢弃不相关的细节。骨架化降低了输入数据的复杂性，使模型更容易关注相关的形态特征。

3. 重新采样骨架坐标：重新采样骨架坐标以生成密集的骨架点，从而更好地表示线虫的结构。这确保了数据的一致性，每个图像都由固定数量的骨架点表示。这些点用于固定线性化过程。

4. 线虫图像的线性化：然后使用重新采样的骨架点进行线性化。线虫的身体沿着其整体方向拉直，将图像分割成小部分或“标记”，然后重新组装以模仿拉直的线虫。此步骤减少了由不同姿势（例如卷曲或弯曲）引起的变化，使模型能够以标准化形式分析线虫。

5. 形态特征标准化：通过将每条线虫转换为标准化的线性图像，WormCNN 可确保形态特征（例如身体厚度、长度和其他与年龄相关的变化）在所有图像中保持一致。这种标准化表示使模型能够专注于与衰老或健康状况相关的细微生物特征变化，而不会被无关的姿势变化所分散注意力。

Image capture and data collection: Worm images are captured daily using the SingleWorm method in a controlled environment, where worms are cultured individually in 384-well plates. This method minimizes interactions between worms and ensures that the images collected accurately represent the morphological state of each individual without interference from other worms. Images are typically captured in grayscale, simplifying processing and reducing computational load.

Skeletonization and feature extraction: During preprocessing, each worm’s image is converted into a skeleton structure. This process involves detecting the worm's body and simplifying it into a skeleton line, preserving key structural features like body curvature while discarding irrelevant details. Skeletonization reduces the complexity of the input data, making it easier for the model to focus on relevant morphological features.

Resampling skeleton coordinates: The skeleton coordinates are resampled to generate dense skeleton points, providing a better representation of the worm’s structure. This ensures data consistency, with each image represented by a fixed number of skeleton points. These points are used to facilitate the linearization process.

Linearization of worm images: The resampled skeleton points are then used to linearize the worm. The worm’s body is straightened along its overall direction, dividing the image into small segments or "tokens," which are then reassembled to mimic a straightened worm. This step reduces variations caused by different poses, such as curling or bending, enabling the model to analyze the worms in a standardized form.

Morphological feature standardization: By converting each worm into a standardized linear image, WormCNN ensures that morphological features (such as body thickness, length, and other age-related changes) remain consistent across all images. This standardized representation allows the model to focus on subtle biological feature changes related to aging or health status without being distracted by irrelevant pose variations.

WormCNN Architecture架构

The WormCNN model is built on a convolutional neural network framework, which is well-suited for image recognition tasks. The architecture consists of multiple layers designed to automatically learn and extract relevant features from C. elegans images. The workflow of the model can be summarized as follows:模型建立在非常适合图像识别任务的卷积神经网络框架之上。该架构由多个层组成，旨在自动学习和提取秀丽隐杆线虫图像中的相关特征。该模型的工作流程可总结如下：

Input layer: The model takes in preprocessed images of C. elegans, typically in grayscale, for simplicity and reduced computational complexity.

1. 输入层：该模型接受秀丽隐杆线虫的预处理图像，通常为灰度，以简单起见并降低计算复杂性。

2. 卷积层：这些层负责特征提取。WormCNN 使用多组过滤器来检测图像中的各种特征，例如边缘、曲线和纹理。

3. 激活函数：非线性激活函数，例如整流线性单元 (ReLU)，用于将非线性引入模型，使其能够学习更复杂的模式。

4. 池化层：这些层减少了特征图的空间维度，从而减少了网络中的参数数量和计算量。

5. 全连接层：网络的最后层整合提取的特征并做出分类决策。

6. 输出层： WormCNN 通常在输出层使用 S 型激活函数进行二元分类任务，例如区分年轻蠕虫和年老蠕虫。

Convolutional layers: These layers are responsible for feature extraction. WormCNN uses multiple sets of filters to detect various features in the images, such as edges, curves, and textures.应用

Activation functions: Non-linear activation functions, such as the rectified linear unit (ReLU), introduce non-linearity into the model, allowing it to learn more complex patterns.

Pooling layers: These layers reduce the spatial dimensions of the feature maps, decreasing the number of parameters and computations in the network.

Fully connected layers: The final layers of the network integrate the extracted features and make classification decisions.

Output layer: WormCNN typically uses a sigmoid activation function in the output layer for binary classification tasks, such as distinguishing between young and old worms.在生物研究中有广泛的应用：

Applications

1. 衰老研究：通过根据秀丽隐杆线虫的形态对其年龄进行分类，WormCNN 可以为衰老以及各种干预措施对寿命的影响的研究做出贡献。

2. 发育生物学：该模型可用于追踪发育阶段并识别任何异常或基因突变的影响。

3. 遗传学研究：在涉及基因编辑或敲除的研究中，WormCNN 可以通过分析蠕虫的物理特征来帮助进行表型分析。

4. 高通量筛选：在需要进行大规模图像分析的实验室中，WormCNN 可以实现流程自动化并提高数据分析效率。

WormCNN has a wide range of applications in biological research:结论

• Aging research: By classifying C. elegans by age based on their morphology, WormCNN can contribute to studies on aging and the effects of various interventions on lifespan.
• Developmental biology: The model can be used to track developmental stages and identify the effects of any abnormalities or genetic mutations.
• Genetic research: In studies involving gene editing or knockouts, WormCNN can assist in phenotype analysis by examining the worms' physical traits.
• High-throughput screening: In laboratories requiring large-scale image analysis, WormCNN can automate the process and improve data analysis efficiency.

Conclusion

WormCNN represents a novel approach in computational biology, providing a tool for automated image analysis of C. elegans. Its ability to accurately classify and analyze images of these worms has the potential to accelerate research in various biological fields.代表了计算生物学领域的一种新方法，为秀丽隐杆线虫的自动图像分析提供了一种工具。它能够准确地对这些线虫的图像进行分类和分析，有可能加速各种生物学科的研究。