Unlabeled Regions Research Articles

Exploring better sparsely annotated shadow detection

Sparsely annotated image segmentation has attracted increasing attention due to its low labeling cost. However, existing weakly-supervised shadow detection methods require complex training procedures, and there is still a significant performance gap compared to fully-supervised methods. This paper summarizes two current challenges in sparsely annotated shadow detection, i.e., weak supervision diffusion and poor structure recovery, and attempts to alleviate them. To this end, we propose a one-stage weakly-supervised learning framework to facilitate sparsely annotated shadow detection. Specifically, we first design a simple yet effective semantic affinity module (SAM) that adaptively propagates scribble supervision to unlabeled regions using a gradient diffusion scheme. Then, to better recover shadow structures, we introduce a feature-guided edge-aware loss, which leverages higher-level semantic relations to perceive shadow boundaries, while avoiding interference from ambiguous regions. Finally, we present an intensity-guided structure consistency loss to ensure that the same images with different brightness are predicted to be consistent shadow masks, which can be regarded as a self-consistent mechanism to improve the model’s generalization ability. Experimental results on three benchmark datasets demonstrate that our approach significantly outperforms previous weakly-supervised methods and achieves competitive performance in comparison to recent state-of-the-art fully-supervised methods.

Leveraging Input-Level Feature Deformation with Guided-Attention for Sulcal Labeling.

The identification of cortical sulci is key for understanding functional and structural development of the cortex. While large, consistent sulci (or primary/secondary sulci) receive significant attention in most studies, the exploration of smaller and more variable sulci (or putative tertiary sulci) remains relatively under-investigated. Despite its importance, automatic labeling of cortical sulci is challenging due to (1) the presence of substantial anatomical variability, (2) the relatively small size of the regions of interest (ROIs) compared to unlabeled regions, and (3) the scarcity of annotated labels. In this paper, we propose a novel end-to-end learning framework using a spherical convolutional neural network (CNN). Specifically, the proposed method learns to effectively warp geometric features in a direction that facilitates the labeling of sulci while mitigating the impact of anatomical variability. Moreover, we introduce a guided-attention mechanism that takes into account the extent of deformation induced by the learned warping. This extracts discriminative features that emphasize sulcal ROIs, while suppressing irrelevant information of unlabeled regions. In the experiments, we evaluate the proposed method on 8 sulci of the posterior medial cortex. Our method outperforms existing methods particularly in the putative tertiary sulci. The code is publicly available at https://github.com/Shape-Lab/DSPHARM-Net.

Semi-Supervised Left-Atrial Segmentation Based on Squeeze–Excitation and Triple Consistency Training

Convolutional neural networks (CNNs) have achieved remarkable success in fully supervised medical image segmentation tasks. However, the acquisition of large quantities of homogeneous labeled data is challenging, making semi-supervised training methods that rely on a small amount of labeled data and pseudo-labels increasingly popular in recent years. Most existing semi-supervised learning methods, however, underestimate the importance of the unlabeled regions during training. This paper posits that these regions may contain crucial information for minimizing the model’s uncertainty prediction. To enhance the segmentation performance of the left-atrium database, this paper proposes a triple consistency segmentation network based on the squeeze-and-excitation mechanism (SETC-Net). Specifically, the paper constructs a symmetric architectural unit called SEConv, which adaptively recalibrates the feature responses in the channel direction by modeling the inter-channel correlations. This allows the network to adaptively weigh each channel according to the task’s needs, thereby emphasizing or suppressing different feature channels. Moreover, SETC-Net is composed of an encoder and three slightly different decoders, which convert the prediction discrepancies among the three decoders into unsupervised loss through a constructed iterative pseudo-labeling scheme, thus encouraging consistent and low-entropy predictions. This allows the model to gradually capture generalized features from these challenging unmarked regions. We evaluated the proposed SETC-Net on the public left-atrium (LA) database. The proposed method achieved an excellent Dice score of 91.14% using only 20% of the labeled data. The experiments demonstrate that the proposed SETC-Net outperforms seven current semi-supervised methods in left-atrium segmentation and is one of the best semi-supervised segmentation methods on the LA database.

Towards sparse sensor annotations: Uncertainty-based active transfer learning for airfoil flow field prediction

Deep learning has been widely applied in surrogate modeling for airfoil flow field prediction. The success of deep learning relies heavily on large-scale, high-quality labeled samples. However, acquiring labeled samples with complete annotations is prohibitively expensive, and the available annotations in practical engineering are often sparse due to limited observation. To leverage samples with sparse annotations, this paper proposes an uncertainty-based active transfer learning method. The most valuable positions in the flow field are selected based on uncertainty for annotation, effectively improving prediction accuracy and reducing annotation costs. Our method involves a novel active annotation based on synchronous quantile regression, which can mitigate the computational cost of query annotation. Besides, a novel quantile levels-based consistency regularization is proposed to constrain the remaining unlabeled regions and further improve the model performance. Experiments show that our method can significantly reduce prediction errors with only 1% extra annotations, and is a promising tool for achieving rapid and accurate flow field prediction.

Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting.

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and make the first attempt to reduce spatial labeling redundancy for semi-supervised crowd counting. First, instead of annotating all the regions in each crowd image, we propose to annotate the representative ones only. We analyze the region representativeness from both vertical and horizontal directions of initially estimated density maps, and formulate them as cluster centers of Gaussian Mixture Models. Additionally, to leverage the rich unlabeled regions, we exploit the similarities among individuals in each crowd image to directly supervise the unlabeled regions via feature propagation instead of the error-prone label propagation employed in the previous methods. In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions. Extensive experiments on the widely-used benchmarks demonstrate that our method can outperform previous best approaches by a large margin.

A progressive segmentation with weight contrast label enhancement for weakly supervised video salient object detection

AbstractScribble labels have gained increasing attention in the field of weakly supervised video salient object detection (VSOD). Based on scribble labels, latest methods can spread labeled pixels to unlabeled regions using local coherence loss, but predicted objects often lose detail and boundary information. In this work, a novel method based on back‐foreground weight contrast is proposed that adds label enhancement points to facilitate the model to learn the edge, detail and location of salient object. Additionally, a new VSOD framework based on global structural localization is introduced. Enhanced scribble labels are used to assist the model for global localization, and then the located regions are finely segmented by the trained model. Extensive experiments demonstrate that the method achieves the state‐of‐the‐art performance on common VSOD datasets, with an improvement of 3.75%, 4.68%, and 0.88% in S‐measure, F‐measure, and MAE, respectively.

CVCL: Context-aware Voxel-wise Contrastive Learning for label-efficient multi-organ segmentation

Despite the significant performance improvement on multi-organ segmentation with supervised deep learning-based methods, the label-hungry nature hinders their applications in practical disease diagnosis and treatment planning. Due to the challenges in obtaining expert-level accurate, densely annotated multi-organ datasets, label-efficient segmentation, such as partially supervised segmentation trained on partially labeled datasets or semi-supervised medical image segmentation, has attracted increasing attention recently. However, most of these methods suffer from the limitation that they neglect or underestimate the challenging unlabeled regions during model training. To this end, we propose a novel Context-aware Voxel-wise Contrastive Learning method, referred as CVCL, to take full advantage of both labeled and unlabeled information in label-scarce datasets for a performance improvement on multi-organ segmentation. Experimental results demonstrate that our proposed method achieves superior performance than other state-of-the-art methods.

Analyzing Single Cell Secretions by "Shadow Imaging".

Here, we describe a method, which we term "shadow imaging," to analyze the secretions of individual cells at immune synapses or other cell contacts. Following immune synapse formation and cellular activation on ligand-rich slides, the position of each cell is recorded using a pulsed immunofluorescence stain against the proteins on the ligand-rich slide surface. The pulsed stain does not penetrate the synaptic cleft, resulting in an unlabeled region or "shadow" beneath cells that is retained following cellular detachment. The secreted components, such as perforin, exosomes, or other types of extracellular vesicles, are retained on the slide and can be analyzed on a single-cell basis using immunofluorescence. The ability to identify single cells secreting different combinations of particles, proteins, and vesicles enables us to better understand the heterogeneity in immune cell secretions and can be used as a novel approach for phenotyping cell populations.

BLPSeg: Balance the Label Preference in Scribble-Supervised Semantic Segmentation.

Scribble-supervised semantic segmentation is an appealing weakly supervised technique with low labeling cost. Existing approaches mainly consider diffusing the labeled region of scribble by low-level feature similarity to narrow the supervision gap between scribble labels and mask labels. In this study, we observe an annotation bias between scribble and object mask, i.e., label workers tend to scribble on the spacious region instead of corners. This label preference makes the model learn well on those frequently labeled regions but poor on rarely labeled pixels. Therefore, we propose BLPSeg to balance the label preference for complete segmentation. Specifically, the BLPSeg first predicts an annotation probability map to evaluate the rarity of labels on each image, then utilizes a novel BLP loss to balance the model training by up-weighting those rare annotations. Additionally, to further alleviate the impact of label preference, we design a local aggregation module (LAM) to propagate supervision from labeled to unlabeled regions in gradient backpropagation. We conduct extensive experiments to illustrate the effectiveness of our BLPSeg. Our single-stage method even outperforms other advanced multi-stage methods and achieves state-of-the-art performance.

Weakly supervised skin lesion segmentation based on spot‐seeds guided optimal regions

Automatic skin lesion segmentation is the most critical and relevant task in computer-aided skin cancer diagnosis. Methods based on convolutional neural networks (CNNs) are mainly used in current skin lesion segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve semantic segmentation of skin lesion by CNNs. In this paper, a novel weakly supervised framework for skin lesion segmentation is presented, which generates high-quality pixel-level annotations and optimizes the segmentation network. A hierarchical image segmentation algorithm can predict a boundary map for training images. Then, the optimal regions of candidate hierarchical levels are selected. Afterward, Superpixels-CRF built on the optimal regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, a segmentation network can be trained and segmentation masks can be predicted. To iteratively optimize the segmentation network, the predicted segmentation masks are refined and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework reduces the gap between weakly and fully supervised skin lesion segmentation methods, and achieves state-of-the-art performance while reducing human labeling efforts.

Weakly Supervised Building Semantic Segmentation Based on Spot-Seeds and Refinement Process.

Automatic building semantic segmentation is the most critical and relevant task in several geospatial applications. Methods based on convolutional neural networks (CNNs) are mainly used in current building segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve the semantic segmentation of building by CNNs. In this paper, we propose a novel weakly supervised framework for building segmentation, which generates high-quality pixel-level annotations and optimizes the segmentation network. A superpixel segmentation algorithm can predict a boundary map for training images. Then, Superpixels-CRF built on the superpixel regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, we can train a more robust segmentation network and predict segmentation maps. To iteratively optimize the segmentation network, the predicted segmentation maps are refined, and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework achieves a marked improvement in the building’s segmentation quality while reducing human labeling efforts.

Look Here: Learning Geometrically Consistent Refinement of Inverse-Depth Images for 3D Reconstruction

Building good 3D maps is a challenging and expensive task, which requires high-quality sensors and careful, time-consuming scanning. We seek to reduce the cost of building good reconstructions by correcting views of existing low-quality ones in a post-hoc fashion using learnt priors over surfaces and appearance. We train a convolutional neural network model to predict the difference in inverse-depth from varying viewpoints of two meshes — one of low-quality that we wish to correct, and one of high-quality that we use as a reference. Our full model runs at 11.3[Formula: see text]Hz when aggregating four input views. In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes. We address this with a suitable network architecture, and introduce a loss-weighting mechanism that emphasizes edges in the prediction. Furthermore, smooth predictions result in geometrical inconsistencies. To deal with this issue, we present a loss function which penalizes re-projection differences that are not due to occlusions. Future applications of this work will incorporate semantic scene understanding in a multi-task learning setting. We explore the efficacy of the proposed system in terms of gross error correction and generalization capability by showing its performance in practice on a subset of the Kitti Odometry dataset, complete with a component-wise ablation study. We evaluate correctness and completeness measures of surface reconstruction across viewpoints and show that the proposed system is introspective in regions lacking sufficient high-quality supervision — indeed, models trained with geometric consistency loss create a lot more surface in areas that were not supervised, in one case filling in 67.97% or 8010[Formula: see text]m2 of an unlabeled input region. Finally, we assess the practical applicability of our method at large-scale by experiments over the full scope of the Kitti Odometry dataset. Broadly, as a measure of effectiveness, our model reduces gross errors by 45.3–77.5%, up to five times more than previous work. We also assess the practical applicability of our method to 3D reconstruction at large scales and find that compared to the baseline our model shows better stability in correctness when improving completeness of surfaces, and is effective in reducing median total error by up to 21.8[Formula: see text]cm.

Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures. We design a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input, which could be viewed as a regularization technique to enhance the model generalization ability. Additionally, we design an aggregation module (AGGM) to better integrate high-level features, low-level features and global context information for the decoder to aggregate various information. Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: Fβ = 0.8995, Eξ = 0.9079 and MAE = 0.0489), with an average gain of 4.60% for F-measure, 2.05% for E-measure and 1.88% for MAE over the previous best performing method on this task. Source code is available at http://github.com/siyueyu/SCWSSOD.

Improving the predictions of soil properties from VNIR–SWIR spectra in an unlabeled region using semi-supervised and active learning

Monitoring the status of the soil ecosystem to identify the spatio-temporal extent of the pressures exerted and mitigate the effects of climate change and land degradation necessitates the need for reliable and cost-effective solutions. To address this need, soil spectroscopy in the visible, near- and shortwave-infrared (VNIR–SWIR) has emerged as a viable alternative to traditional analytical approaches. To this end, large-scale soil spectral libraries coupled with advanced machine learning tools have been developed to infer the soil properties from the hyperspectral signatures. However, models developed from one region may exhibit diminished performance when applied to a new, unseen by the model, region due to the large and inherent soil variability (e.g. pedogenetical differences, diverse soil types etc.). Given an existing spectral library with labeled data and a new unlabeled region (i.e. where no soil samples are analytically measured) the question then becomes how to best develop a model which can more accurately predict the soil properties of the unlabeled region.In this paper, a machine learning technique leveraging on the capabilities of semi-supervised learning which exploits the predictors’ distribution of the unlabeled dataset and of active learning which expertly selects a small set of data from the unlabeled dataset as a spiking subset in order to develop a more robust model is proposed. The semi-supervised learning approach is the Laplacian Support Vector Regression following the manifold regularization framework. As far as the active learning component is concerned, the pool-based approach is utilized as it best matches with the aforementioned use-case scenario, which iteratively selects a subset of data from the unlabeled region to spike the calibration set. As a query strategy, a novel machine learning–based strategy is proposed herein to best identify the spiking subset at each iteration. The experimental analysis was conducted using data from the Land Use and Coverage Area Frame Survey of 2009 which covered most of the then member-states of the European Union, and in particular by focusing on the mineral cropland soil samples from 5 different countries. The statistical analysis conducted ascertained the efficacy of our approach when compared to the current state-of-the-art in soil spectroscopy.

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images.

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

Robust Nucleus Detection With Partially Labeled Exemplars

Quantitative analysis of cell nuclei in microscopic images is an essential yet challenging source of biological and pathological information. The major challenge is accurate detection and segmentation of densely packed nuclei in images acquired under a variety of conditions. Mask R-CNN-based methods have achieved state-of-the-art nucleus segmentation. However, the current pipeline requires fully annotated training images, which are time consuming to create and sometimes noisy. Importantly, nuclei often appear similar within the same image. This similarity could be utilized to segment nuclei with only partially labeled training examples. We propose a simple yet effective region-proposal module for the current Mask R-CNN pipeline to perform few-exemplar learning. To capture the similarities between unlabeled regions and labeled nuclei, we apply decomposed self-attention to learned features. On the self-attention map, we observe strong activation at the centers and edges of all nuclei, including unlabeled nuclei. On this basis, our region-proposal module propagates partial annotations to the whole image and proposes effective bounding boxes for the bounding box-regression and binary mask-generation modules. Our method effectively learns from unlabeled regions thereby improving detection performance. We test our method with various nuclear images. When trained with only 1/4 of the nuclei annotated, our approach retains a detection accuracy comparable to that from training with fully annotated data. Moreover, our method can serve as a bootstrapping step to create full annotations of datasets, iteratively generating and correcting annotations until a predetermined coverage and accuracy are reached. The source code is available at https://github.com/feng-lab/nuclei.

Information-Theoretic Analysis of Input Strokes in Visual Object Cutout

Semantic object cutout serves as a basic unit in various image editing systems. In a typical scenario, users are required to provide several strokes which indicate part of the pixels as image background or objects. However, most existing approaches are passive in the sense of accepting input strokes without checking the consistence with user's intention. Here we argue that an active strategy may potentially reduce the interaction burden. Before any real calculation for segmentation, the program can roughly estimate the uncertainty for each image element and actively provide useful suggestions to users. Such a pre-processing is particularly useful for beginners unaware of feeding the underlying cutout algorithms with optimal strokes. We develop such an active object cutout algorithm, named ActiveCut, which makes it possible to automatically detect ambiguity given current user-supplied strokes, and synthesize "suggestive strokes" as feedbacks. Generally, suggestive strokes come from the ambiguous image parts and have the maximal potentials to reduce label uncertainty. Users can continuously refine their inputs following these suggestive strokes. In this way, the number of user-program interaction iterations can thus be greatly reduced. Specifically, the uncertainty is modeled by mutual information between user strokes and unlabeled image regions. To ensure that ActiveCut works at a user-interactive rate, we adopt superpixel lattice based image representation, whose computation depends on scene complexity rather than original image resolution. Moreover, it retains the 2-D-lattice topology and is thus more suitable for parallel computing. While for the most time-consuming calculation of probabilistic entropy, variational approximation is utilized for acceleration. Finally, based on submodular function theory, we provide a theoretic analysis for the performance lower bound of the proposed greedy algorithm. Various user studies are conducted on the MSRC image dataset to validate the effectiveness of our proposed algorithm.

Structural and Functional Characterization of the C-terminal Transmembrane Region of NBCe1-A

NBCe1-A and AE1 both belong to the SLC4 HCO(3)(-) transporter family. The two transporters share 40% sequence homology in the C-terminal transmembrane region. In this study, we performed extensive substituted cysteine-scanning mutagenesis analysis of the C-terminal region of NBCe1-A covering amino acids Ala(800)-Lys(967). Location of the introduced cysteines was determined by whole cell labeling with a membrane-permeant biotin maleimide and a membrane-impermeant 2-((5(6)-tetramethylrhodamine)carboxylamino) ethyl methanethiosulfonate (MTS-TAMRA) cysteine-reactive reagent. The results show that the extracellular surface of the NBCe1-A C-terminal transmembrane region is minimally exposed to aqueous media with Met(858) accessible to both biotin maleimide and TAMRA and Thr(926)-Ala(929) only to TAMRA labeling. The intracellular surface contains a highly exposed (Met(813)-Gly(828)) region and a cryptic (Met(887)-Arg(904)) connecting loop. The lipid/aqueous interface of the last transmembrane segment is at Asp(960). Our data clearly determined that the C terminus of NBCe1-A contains 5 transmembrane segments with greater average size compared with AE1. Functional assays revealed only two residues in the region of Pro(868)-Leu(967) (a functionally important region in AE1) that are highly sensitive to cysteine substitution. Our findings suggest that the C-terminal transmembrane region of NBCe1-A is tightly folded with unique structural and functional features that differ from AE1.

Interactive object segmentation in digital images by foreground/background region classification with hierarchical queues

In this paper, we present work on interactive object segmentation in digital images. The user can employ the proposed work to separate the target object from the background easily. After marker drawing of foreground and background from a user, the user interface shows the target object in original color and the background in some other specific color, respectively. Compared with the tedious steps of Adobe Photoshop, the user can interactively approximate the contour of the target object with more efficiency. Our proposed algorithm is based on Foreground/Background region classification by comparing the similarity of color information. At first, the user interface processes the input image by watershed segmentation to produce the segmented regions. Then, some unlabeled regions are assigned as foreground regions or background regions by marker drawing. After marker drawing, the remaining unlabeled regions are processed by Foreground/Background region classification. In our implementation, we also introduce hierarchical queues to store the unlabeled regions during the procedure of region classification. The target object is segmented after Foreground/Background region classification. In our experiments, the proposed algorithm provides output with high accuracy and low effort.

The Distribution of Repetitive DNAs Along Chromosomes in Plants Revealed by Self-genomic in situ Hybridization

The distribution of repetitive DNAs along chromosomes is one of the crucial elements for understanding the organization and the evolution of plant genomes. Using a modified genomic in situ hybridization (GISH) procedure, fluorescence in situ hybridization (FISH) with genomic DNA to their own chromosomes (called self-genomic in situ hybridization, self-GISH) was carried out in six selected plant species with different genome size and amount of repetitive DNA. Nonuniform distribution of the fluorescent labeled probe DNA was observed on the chromosomes of all the species that were tested. The signal patterns varied among species and were related to the genome size. The chromosomes of the small Arabidopsis genome were labeled almost only in the pericentromeric regions and the nucleolus organizer regions (NORs). The signals in the relatively small genomes, rice, sorghum, and Brassica oleracea var. capitata L., were dispersed along the chromosome lengths, with a predominant distribution in the pericentromeric or proximal regions and some heterochromatic arms. All chromosomes of the large genomes, maize and barley, were densely labeled with strongly labeled regions and weakly labeled or unlabeled regions being arranged alternatively throughout the lengths. In addition, enhanced signal bands were shown in all pericentromeres and the NORs in B. oleracea var. capitata and in all pericentromeric regions and certain intercalary sites in barley. The enhanced signal band pattern in barley was found consistent with the N-banding pattern of this species. The GISH with self-genomic DNA was compared with FISH with C ot-1 DNA in rice, and their signal patterns are found to be basically consistent. Our results showed that the self-GISH signals actually reflected the hybridization of genomic repetitive DNAs to the chromosomes, thus the self-GISH technique would be useful for revealing the distribution of the regions where repetitive DNAs concentrate along chromosomes and some chromatin differentiation associated with repetitive DNAs in plants.