Fine Labels Research Articles

Tumor classification algorithm via parallel collaborative optimization of single- and multi-objective consistency on PET/CT

Malignant tumors still have a high incidence and mortality rate worldwide. Pathological examination remains the clinical gold standard for tumor diagnosis. However, some patients cannot undergo pathological examination due to advanced age and special lesion location. Therefore, making full use of PET/CT to assist doctors in tumor classification has important clinical significance. Since category labels are calibrated according to pathological images, it is difficult to obtain effective pathological category features directly using PET-CT image modeling. In response to this problem, this paper proposes a novel tumor classification algorithm. This method fully utilizes multi-gray-level 3D gray-level co-occurrence matrix and the proposed rough and fine constraint network under the constraint loss of rough and fine labels. Based on single- and multi-objective consistency, a parallel collaborative optimization method is proposed, including category consistency loss and feature specificity loss. To reduce the interference of redundant features, an improved Boruta feature selection method using multiple classifiers and multiple steps is proposed. The final result is obtained through a conditional weighted voting function. The proposed method shows excellent performance in both the submodels and the fusion model. We validated the proposed tumor classification method on three datasets and achieved good performance with the accuracy of 0.80–0.85 and F1-score of 0.78–0.88. The results indicate that the proposed method has good performance and generalization ability.

Unified weakly and semi-supervised crack segmentation framework using limited coarse labels

Obtaining extensive, high-quality datasets for crack segmentation with pixel-level labels is expensive and labor-intensive. The Unified Weakly and Semi-supervised Crack Segmentation (UWSCS) framework addresses this challenge by leveraging a limited number of images with coarse labels and a larger set of unlabeled images. Two label correction modules, based on super-pixel segmentation and a shrink module, are incorporated in the model training to improve crack label accuracy and optimize edge refinement. UWSCS employs a dual-encoder fusion network, combining transformers and convolutional neural networks, to enhance crack segmentation in complex backgrounds. An enhanced algorithm using the medial axis transform is proposed for accurately quantifying crack length and width. Extensive experiments were conducted on both synthetic and real crack datasets to validate the superior performance of UWSCS. The results underscore the significant impact of label quality and quantity used in training on model prediction accuracy. Trained on a concrete crack dataset with limited coarse labels, UWSCS achieves an Intersection of Union (IoU) of 77.53%, surpassing the fully supervised model using the same number of coarse labels by 28.64%. It closely approaches the performance of a fully supervised model with the same number of fine labels (IoU of 80.21%). UWSCS outperforms other advanced networks and semi-supervised/weakly supervised algorithms when trained with a limited set of more cost-effective manually labeled coarse labels. Integrated with the crack segmentation network, super-pixel segmentation, and shrink modules during training, UWSCS with limited coarse labels performs similarly to a fully supervised model using fine labels, thereby reducing manual labeling costs by over 90% and enhancing detection efficiency in practical engineering.

Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation.

Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.

Fine-grained recognition: Multi-granularity labels and category similarity matrix

The core of fine-grained recognition is to distinguish different subcategories within a same broad category through subtle differences in images. Yet two important factors are less explored: Firstly, the degree of similarity between categories, with the more similar categories being more likely to be confused. Secondly, different fine-grained definitions under divergent levels of expertise, with one coarse label corresponding to multiple similar fine categories (e.g., a brand of car has multiple models). In this paper, we discover that multi-granularity label can function as an intermediary for turning inter-category similarity relations into fine-grained recognition performance. Specifically, we first explore the association between label hierarchies in multi-granularity prediction: both coarse and fine label predictions require coarse information, but fine label prediction additionally requires subtle information. Then, a multi-granularity classification framework is proposed, where the extracted feature is decoupled and re-coupled into groups for predicting labels at different granularities, respectively. The key is to involve coarse-grained features in the prediction of finer-level labels. The realization way is to train these features with a joint probability-based loss that we design to reduce inter-task interference through inter-granularity probability relations. Lastly, for tasks without coarse labels, a category similarity matrix is suggested for measuring inter-class similarity and a further non-parametric aggregation method is devised for clustering fine labels into coarse labels. The evaluation is carried out on several fine-grained classification benchmark datasets. Results show that the proposed approach achieves state-of-the-art in multi-granularity classification and exhibits the potential to enhance existing fine-grained recognition models.

Feature Pyramid Network With Level-Aware Attention for Meningioma Segmentation

Meningiomas are the most common primary intracranial tumors in adults, and they are dangerous and even lethal when they grow and oppress vital organs. In clinical, microsurgical resection is the most widely used treatment for most meningiomas. And tumor segmentation is an essential primary step before applying any therapy. However, due to the various meningioma locations and complicated intracranial structures, it is still challenging to segment the tumor accurately in both boundaries and contour automatically. In this work, a novel method is proposed for the automatic segmentation of meningioma. In general, the proposed method follows a coarse-to-fine strategy. Specifically, the feature pyramid structure is employed to extract multi-level features. Further, a Level-aware Attention is proposed to refine multi-level features by naturally utilizing the complementary features of different levels, thus significantly improving the segmentation performance. Moreover, to validate the proposed method, a realistic dataset of meningioma with fine labels is constructed, and experimental results on the dataset demonstrate the effectiveness of the proposed method.

Automated differentiation of skin melanocytes from keratinocytes in high‐resolution histopathology images using a weakly‐supervised deep‐learning framework

AbstractEarly detection and accurate diagnosis of melanoma skin cancer which accounts for 75% of all skin cancer mortalities would significantly improve survival rates. Melanoma is characterized by an abnormal proliferation of malignant melanocytes in the epidermis of the skin, which is composed predominantly of keratinocytes. Localization and differentiation of melanocytes from keratinocytes in the skin in whole slide images (WSIs) is an important initial task towards the diagnosis of the tumor. Because of the small size of melanocytes and the dominance of keratinocytes in the epidermis, identification of melanocytes can be challenging and prone to errors. We propose a new fully‐automated framework based on deep‐machine learning to identify melanocytes and keratinocytes with detection accuracy of 90.5% and 87.4%, respectively. This framework begins with segmenting the epidermis layer inside 640 × 320 × 3 super tiles of WSIs using a DeepLabV3+ model followed by a weakly‐supervised deep learning approach deploying a fine‐tuned pre‐trained visual geometry group (VGG)‐16 model, gradient‐weighted class activation mapping (Grad‐CAM), Otsu's and contour estimation methods in order to identify melanocytes and keratinocytes inside each 64 × 64 × 3 tile (i.e., a subdivision of the super tile). The proposed framework outperforms the state‐of‐the‐art methods as well as provides a significant improvement of 28% and 17% over the fully supervised faster region‐based convolutional neural network (R‐CNN) in detecting melanocytes and keratinocytes respectively without the need for expensive expert fine labels for model training and validation. The proposed framework offers a promising accurate tool to aid pathologists in differentiating melanocytes from keratinocytes that would eventually support the diagnosis of melanoma.

A pixel-level coarse-to-fine image segmentation labelling algorithm

Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.

A Supervised Segmentation Network for Hyperspectral Image Classification

Recently, deep learning has drawn broad attention in the hyperspectral image (HSI) classification task. Many works have focused on elaborately designing various spectral-spatial networks, where convolutional neural network (CNN) is one of the most popular structures. To explore the spatial information for HSI classification, pixels with its adjacent pixels are usually directly cropped from hyperspectral data to form HSI cubes in CNN-based methods. However, the spatial land-cover distributions of cropped HSI cubes are usually complicated. The land-cover label of a cropped HSI cube cannot simply be determined by its center pixel. In addition, the spatial land-cover distribution of a cropped HSI cube is fixed and has less diversity. For CNN-based methods, training with cropped HSI cubes will result in poor generalization to the changes of spatial land-cover distributions. In this paper, an end-to-end fully convolutional segmentation network (FCSN) is proposed to simultaneously identify land-cover labels of all pixels in a HSI cube. First, several experiments are conducted to demonstrate that recent CNN-based methods show the weak generalization capabilities. Second, a fine label style is proposed to label all pixels of HSI cubes to provide detailed spatial land-cover distributions of HSI cubes. Third, a HSI cube generation method is proposed to generate plentiful HSI cubes with fine labels to improve the diversity of spatial land-cover distributions. Finally, a FCSN is proposed to explore spectral-spatial features from finely labeled HSI cubes for HSI classification. Experimental results show that FCSN has the superior generalization capability to the changes of spatial land-cover distributions.

Conditional generative adversarial network for EEG-based emotion fine-grained estimation and visualization

In the field of affective computing (AC), coarse-grained AC has been developed and widely applied in many fields. Electroencephalogram (EEG) signals contain abundant emotional information. However, it is difficult to develop fine-grained AC due to the lack of fine-grained labeling data and suitable visualization methods for EEG data with fine labels. To achieve a fine mapping of EEG data directly to facial images, we propose a conditional generative adversarial network (cGAN) to establish the relationship between EEG data associated with emotions, a coarse label, and a facial expression image in this study. In addition, a corresponding training strategy is also proposed to realize the fine-grained estimation and visualization of EEG-based emotion. The experiments prove the reasonableness of the proposed method for the generation of fine-grained facial expressions. The image entropy of the generated image indicates that the proposed method can provide a satisfactory visualization of fine-grained facial expressions.

A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-Output Classification Problems

This article describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i.e., when the response variables have dimension higher than one. Such coherent embeddings can be used simultaneously for different tasks, such as identity retrieval and soft biometrics labelling. We propose a generalization of the triplet loss that: 1) defines a metric that considers the number of agreeing labels between pairs of elements; 2) introduces the concept of similar classes, according to the values provided by the metric; and 3) disregards the notion of anchor, sampling four arbitrary elements at each time, from where two pairs are defined. The distances between elements in each pair are imposed according to their semantic similarity (i.e., the number of agreeing labels). Likewise the triplet loss, our proposal also privileges small distances between positive pairs. However, the key novelty is to additionally enforce that the distance between elements of any other pair corresponds inversely to their semantic similarity. The proposed loss yields embeddings with a strong correspondence between the classes centroids and their semantic descriptions. In practice, it is a natural choice to jointly infer coarse (soft biometrics) + fine (ID) labels, using simple rules such as k-neighbours. Also, in opposition to its triplet counterpart, the proposed loss appears to be agnostic with regard to demanding criteria for mining learning instances (such as the semi-hard pairs). Our experiments were carried out in five different datasets (BIODI, LFW, IJB-A, Megaface and PETA) and validate our assumptions, showing results that are comparable to the state-of-the-art in both the identity retrieval and soft biometrics labelling tasks.

Learning Hierarchy Aware Embedding from Raw Audio for Acoustic Scene Classification

Recent advancements in modeling speech and audio signals using deep neural networks have shown that systems learning both features and the classifier can be built directly from raw signal. However, the performance of such end-to-end systems for acoustic scene classification (ASC) task is still not at par with conventional systems built using spectral features. In this work, we propose a raw waveform based end-to-end ASC system using convolutional neural network. In contrast to the existing studies using a non-hierarchical model, our framework leverages the hierarchical relations between acoustic categories to improve the classification performance. To this aim, our multi-task model is trained with coarse and fine labels that correspond to different levels of abstraction. In order to ensure consistency in the encoded information via label hierarchy, the proposed framework uses a prototypical model. Such a model ensures that the learned representations at least match to one of the global categorical learned prototypes. We also employed a statistical pooling layer to aggregate hidden representations over multiple frames of the input audio signal. The statistics (mean and standard deviation) are concatenated together to form a fixed-length audio embedding. This aggregation is done via an attention module so as to guide the model's attention even in presence of relatively short or transient acoustic events. Further, the proposed framework incorporate two parallel feature processing pipelines to achieve different resolutions for extracting important acoustic cues. Various experiments on publicly available datasets are performed to demonstrate the effectiveness of the proposed framework for ASC task. Additional transfer learning experiments showed the proposed model's adaptation capability to unseen data. Network analysis and visualizations demonstrate the importance of individual modules and their impact on overall representation learning for ASC task.

Weakly labeled fine-grained classification with hierarchy relationship of fine and coarse labels

The current work of fine-grained classification generally depends on a large number of fine labels of images. However, these fine labels are much more difficult to annotate than the coarse labels, which generalize fine labels based on the hierarchy of categories. In this paper, we propose to make fine labels prediction under a weakly supervised setting where a subset of training data is labeled with fine labels and the others only have coarse labels. We aim to explore the hierarchy relationship between coarse classes and fine classes to achieve a better performance on fine-grained classification and meanwhile reduce the heavy dependence on fine labels. To this end, we use convolutional block attention module and multi-scale convolution kernel based feature fusion to generate more effective features from multi-scale convolution kernels and multi-level features. Besides, an adaptive classification module exploits the hierarchy relationship of categories to learn the fine-grained classifier automatically according to the available labels of the training data. Comprehensive experiments on the CIFAR100 dataset, a subset of ImageNet and CUB-200-2011 dataset demonstrate the better fine-grained classification performance of our model.

Enhancing Deep Learning with Visual Interactions

Deep learning has emerged as a powerful tool for feature-driven labeling of datasets. However, for it to be effective, it requires a large and finely labeled training dataset. Precisely labeling a large training dataset is expensive, time-consuming, and error prone. In this article, we present a visually driven deep-learning approach that starts with a coarsely labeled training dataset and iteratively refines the labeling through intuitive interactions that leverage the latent structures of the dataset. Our approach can be used to (a) alleviate the burden of intensive manual labeling that captures the fine nuances in a high-dimensional dataset by simple visual interactions, (b) replace a complicated (and therefore difficult to design) labeling algorithm by a simpler (but coarse) labeling algorithm supplemented by user interaction to refine the labeling, or (c) use low-dimensional features (such as the RGB colors) for coarse labeling and turn to higher-dimensional latent structures that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets and a user study.

WSF-NET: Weakly Supervised Feature-Fusion Network for Binary Segmentation in Remote Sensing Image

Binary segmentation in remote sensing aims to obtain binary prediction mask classifying each pixel in the given image. Deep learning methods have shown outstanding performance in this task. These existing methods in fully supervised manner need massive high-quality datasets with manual pixel-level annotations. However, the annotations are generally expensive and sometimes unreliable. Recently, using only image-level annotations, weakly supervised methods have proven to be effective in natural imagery, which significantly reduce the dependence on manual fine labeling. In this paper, we review existing methods and propose a novel weakly supervised binary segmentation framework, which is capable of addressing the issue of class imbalance via a balanced binary training strategy. Besides, a weakly supervised feature-fusion network (WSF-Net) is introduced to adapt to the unique characteristics of objects in remote sensing image. The experiments were implemented on two challenging remote sensing datasets: Water dataset and Cloud dataset. Water dataset is acquired by Google Earth with a resolution of 0.5 m, and Cloud dataset is acquired by Gaofen-1 satellite with a resolution of 16 m. The results demonstrate that using only image-level annotations, our method can achieve comparable results to fully supervised methods.

CNN-RNN: a large-scale hierarchical image classification framework

Objects are often organized in a semantic hierarchy of categories, where fine-level categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

Designing a symmetric classifier for image annotation using multi-layer sparse coding

Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval. This problem can be viewed as a label-assignment problem by a classifier dealing with a very large set of labels, i.e., the vocabulary set. We propose a novel annotation method that employs two layers of sparse coding and performs coarse-to-fine labeling. Themes extracted from the training data are treated as coarse labels. Each theme is a set of training images that share a common subject in their visual and textual contents. Our system extracts coarse labels for training and test images without requiring any prior knowledge. Vocabulary words are the fine labels to be associated with images. Most of the annotation methods achieve low recall due to the large number of available fine labels, i.e., vocabulary words. These systems also tend to achieve high precision for highly frequent words only. On the other hand, text mining literature discusses a general trend where relatively rare/moderately frequent words are more important for search retrieval process than the extremely frequent words. Our system not only outperforms various previously proposed annotation systems, but also achieves symmetric response in terms of precision and recall. Our system scores and maintains high precision for words with a wide range of frequencies. Such behavior is achieved by intelligently reducing the number of available fine labels or words for each image based on coarse labels assigned to it.

A Hierarchical Image Annotation Method Based on SVM and Semi-supervised EM

Automatic image annotation, which aims at automatically identifying and then assigning semantic Keywords to the meaningful objects in a digital image, is not a very difficult task for human but has been regarded as a difficult and challenging problem to machines. In this paper, we present a hierarchical annotation scheme considering that generally human s visual identification to a scenery object is a rough-to-fine hierarchical process. First, the input image is segmented into multiple regions and each segmented region is roughly labeled with a general keyword using the multi-classification support vector machine. Since the results of rough annotation affect fine annotation directly, we construct the statistical contextual relationship to revise the improper labels and improve the accuracy of rough annotation. To obtain reasonable fine annotation for those roughly classified regions, we propose an active semi-supervised expectation-maximization algorithm, which can not only find the representative pattern of each fine class but also classify the roughly labeled regions into corresponded fine classes. Finally, the contextual relationship is applied again to revise the improper fine labels. To illustrate the effectiveness of the presented approaches, a prototype image annotation system is developed, the preliminary results of which showed that the hierarchical annotation scheme is effective.

A Hierarchical Image Annotation Method Based on SVM and Semi-supervised EM

Automatic image annotation, which aims at automatically identifying and then assigning semantic keywords to the meaningful objects in a digital image, is not a very difficult task for human but has been regarded as a difficult and challenging problem to machines. In this paper, we present a hierarchical annotation scheme considering that generally human s visual identification to a scenery object is a rough-to-fine hierarchical process. First, the input image is segmented into multiple regions and each segmented region is roughly labeled with a general keyword using the multi-classification support vector machine. Since the results of rough annotation affect fine annotation directly, we construct the statistical contextual relationship to revise the improper labels and improve the accuracy of rough annotation. To obtain reasonable fine annotation for those roughly classified regions, we propose an active semi-supervised expectation-maximization algorithm, which can not only find the representative pattern of each fine class but also classify the roughly labeled regions into corresponded fine classes. Finally, the contextual relationship is applied again to revise the improper fine labels. To illustrate the effectiveness of the presented approaches, a prototype image annotation system is developed, the preliminary results of which showed that the hierarchical annotation scheme is effective.

Rat interleukin-1 beta binding sites in rat hypothalamus and pituitary gland.

In this study, radiolabeled recombinant rat interleukin-1 beta (r125I-IL-1 beta) was used to localize and characterize IL-1 beta binding in rat hypothalamus and pituitary gland by quantitative autoradiography. The ability of this ligand to bind to type I IL-1 receptor was first tested on murine lymphoma cells (EL-4). In the rat-tissue sections, high densities of specific r125I-IL-1 beta binding sites were localized in the anterior as well as the posterior pituitary and in the choroid plexus. A fine labeling was observed in meninges and third ventricle walls while no binding was detected in the hypothalamic nuclei. Saturation experiments, in the anterior and posterior pituitary, revealed one specific binding site with an affinity constant (Kd) of 0.5 nM. Competition experiments were achieved using either rat IL-1 beta (rIL-1 beta) or human IL-1s (hIL-1 alpha, hIL-1 beta and IL-1 receptor antagonist: hIL-1a). Affinity constants (Ki) were drastically different according to the ligand used, while Ki values were found similar in anterior and posterior pituitary. Competition with rIL-1 beta revealed one binding affinity (Ki of 0.1 nM range). In contrast, competition with hIL-1 beta revealed two binding affinities: a high (Ki: 0.1 pM range) and a low one (Ki: 1 nM range). Competition with hIL-1ra was obtained for high concentrations only (Ki: 10-100 nM range), whereas human IL-1 alpha (hIL-1 alpha) was unable to compete at 1-100 nM.(ABSTRACT TRUNCATED AT 250 WORDS)

Spinal cord projections from hindlimb muscle nerves in the rat studied by transganglionic transport of horseradish peroxidase, wheat germ agglutinin conjugated horseradish peroxidase, or horseradish peroxidase with dimethylsulfoxide.

The spinal cord projections of four different groups of hindlimb muscle nerve branches--the medial and lateral gastrocnemius nerves, muscle branches of the deep peroneal nerve, muscle branches of the femoral nerve, and a nerve to the hamstring muscles--were studied with transganglionic transport of horseradish peroxidase (HRP) in the rat. The influence of varying the postoperative survival (3, 6, and 10 days) and of using wheat germ agglutinin-HRP conjugate (WGA-HRP), or HRP with dimethylsulfoxide (DMSO) instead of free HRP was studied for the gastrocnemius nerves. After 3 days' survival following application of HRP to the gastrocnemius nerves, fine granular labeling was found mainly in lamina V in L4-5, and coarse granular labeling was found in Clarke's column as far caudally as L2, and in laminae VI and VII predominantly in Th12-L2. After 6 or 10 days' survival, the fine labeling in lamina V was sparse or absent, whereas the coarse labeling appeared to remain or to be only slightly reduced in Clarke's column and in laminae VI and VII. No labeling suggestive of terminals was observed in laminae I-III from the gastrocnemius nerves. Except for sparse labeling in lamina I in some of the cases and some minor differences rostrocaudally, the spinal distribution of labeling was similar to that from the other nerves investigated. The distribution of labeling obtained after application of WGA-HRP or HRP with DMSO to the gastrocnemius nerves was very similar to that obtained with free HRP after 3 days' survival. The results indicate that the spinal cord projections of hindlimb muscle nerves in the rat distribute mainly in the deep part of the dorsal horn and in the intermediate zone. Furthermore, the lack of labeling suggestive of terminals in laminae I-III from the gastrocnemius nerves suggests, in conflict with earlier findings in the cat, that primary afferent fibers from muscles do not necessarily terminate in these laminae in the rat. The results suggest, furthermore, that fine granular labeling found in lamina V represents fine-calibered afferent fibers. Finally, the similar spinal projection patterns of the different muscle nerves investigated suggest either a less developed or an essentially different somatotopic organization for muscle afferents compared to cutaneous afferents, as revealed in earlier studies.