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This study introduces OpenMAP‐T1, a deep‐learning‐based method for rapid and accurate whole‐brain parcellation in T1‐ weighted brain MRI, which aims to overcome the limitations of conventional normalization‐to‐atlas‐based approaches and multi‐atlas label‐fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization‐to‐atlas‐based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP‐T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull‐stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP‐T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real‐world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP‐T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP‐T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging. [ABSTRACT FROM AUTHOR] more...

We propose two essential techniques to effectively train generative adversarial network-based super-resolution networks for brain magnetic resonance images, even when only a small number of training samples are available. First, stochastic patch sampling is proposed, which increases training samples by sampling many small patches from the input image. However, sampling patches and combining them causes unpleasant artifacts around patch boundaries. The second proposed method, an artifact-suppressing discriminator, suppresses the artifacts by taking two-channel input containing an original high-resolution image and a generated image. With the introduction of the proposed techniques, the network achieved generation of natural-looking MR images from only ~40 training images, and improved the area-under-curve score on Alzheimer's disease from 76.17% to 81.57%. [ABSTRACT FROM AUTHOR] more...

Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test data sets from new environments. In this article, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Due to its own attention mechanism, our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments with five-class cucumber disease classification show that data augmentation with vanilla CycleGAN cannot help to improve the generalization, i.e., disease diagnostic performance increased by only 0.7% from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4%. We also visually confirmed that the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN. The code is available publicly at https://github.com/IyatomiLab/LeafGAN. Note to Practitioners Automated plant disease diagnosis systems play an important role in the agricultural automation field. Building a practical image-based automatic plant diagnosis system requires collecting a wide variety of disease images with reliable label information. However, it is quite labor-intensive. Conventional systems have reported relatively high diagnosis performance, but most of their scores were largely biased due to the “latent similarity” between training and test images, and their true diagnosis capabilities were much lower than claimed. To address this issue, we propose LeafGAN, which generates countless diverse and high-quality training images; it works as an efficient data augmentation for the diagnosis classifier. Such generated images can be used as useful resources for improving the performance of the cucumber disease diagnosis systems. [ABSTRACT FROM AUTHOR] more...

Featured Application: This work can be applied to support decisions to discontinue ad creatives, which is expected to reduce the load of ad operators. Currently, our framework is deployed in a production environment and provides predictions for our internal operators. Based on the predictions, we have discussions with the operations team to improve the operation efficiency. Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than that for effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found that there are two types of discontinuation: short-term (i.e., cut-out) and long-term (i.e., wear-out). In this paper, we propose a practical prediction framework for the discontinuation of ad creatives with a hazard function-based loss function inspired by survival analysis. Our framework predicts the discontinuations with a multi-modal deep neural network that takes as input the ad creative (e.g., text, categorical, image, numerical features). To improve the prediction performance for the two different types of discontinuations and for the ad creatives that contribute to sales, we introduce two new techniques: (1) a two-term estimation technique with multi-task learning and (2) a click-through rate-weighting technique for the loss function. We evaluated our framework using the large-scale ad creative dataset, including 10 billion scale impressions. In terms of the concordance index (short: 0.896, long: 0.939, and overall: 0.792), our framework achieved significantly better performance than the conventional method (0.531). Additionally, we confirmed that our framework (i) demonstrated the same degree of discontinuation effect as manual operations for short-term cases, and (ii) accurately predicted the ad discontinuation order, which is important for long-running ad creatives for long-term cases. [ABSTRACT FROM AUTHOR] more...

This paper proposes a new computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an aid for detection of skin cancers. Several researchers have developed methods to distinguish between melanoma and nevus, which are both categorized as MSL. However, most of these studies did not focus on NoMSLs such as basal cell carcinoma (BCC), the most common skin cancer and seborrheic keratosis (SK) despite their high incidence rates. It is preferable to deal with these NoMSLs as well as MSLs especially for the potential users who are not enough capable of diagnosing pigmented skin lesions on their own such as dermatologists in training and physicians with different expertise. We developed a new method to distinguish among melanomas, nevi, BCCs, and SKs. Our method calculates 828 candidate features grouped into three categories: color, subregion, and texture. We introduced two types of classification models: a layered model that uses a task decomposition strategy and flat models to serve as performance baselines. We tested our methods on 964 dermoscopy images: 105 melanomas, 692 nevi, 69 BCCs, and 98 SKs. The layered model outperformed the flat models, achieving detection rates of 90.48%, 82.51%, 82.61%, and 80.61% for melanomas, nevi, BCCs, and SKs, respectively. We also identified specific features effective for the classification task including irregularity of color distribution. The results show promise for enhancing the capability of the computer-aided skin lesion classification. [ABSTRACT FROM PUBLISHER] more...

Ductal carcinoma in situ (DCIS) is a pre-invasive carcinoma of the breast that exhibits several distinct morphologies but the link between morphology and patient outcome is not clear. We hypothesize that different mechanisms of growth may still result in similar 2D morphologies, which may look different in 3D. To elucidate the connection between growth and 3D morphology, we reconstruct the 3D architecture of cribriform DCIS from resected patient material. We produce a fully automated algorithm that aligns, segments, and reconstructs 3D architectures from microscopy images of 2D serial sections from human specimens. The alignment algorithm is based on normalized cross correlation, the segmentation algorithm uses histogram equilization, Otsu's thresholding, and morphology techniques to segment the duct and cribra. The reconstruction method combines these images in 3D. We show that two distinct 3D architectures are indeed found in samples whose 2D histological sections are similarly identified as cribriform DCIS. These differences in architecture support the hypothesis that luminal spaces may form due to different mechanisms, either isolated cell death or merging fronds, leading to the different architectures. We find that out of 15 samples, 6 were found to have 'bubble-like' cribra, 6 were found to have 'tube-like' criba and 3 were 'unknown.' We propose that the 3D architectures found, 'bubbles' and 'tubes', account for some of the heterogeneity of the disease and may be prognostic indicators of different patient outcomes. [ABSTRACT FROM AUTHOR] more...

Background: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it. Methods: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm. Results: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method). Conclusion: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images. [ABSTRACT FROM AUTHOR] more...

This paper presents a versatile image recognition method, combining conventional pattern matching and a learning system. The proposed system first quickly determines candidate regions containing the target object in the image, and then examines the result by using the learning system. In the pattern matching, “active search” is used, which quickly extracts the target object using multiple two-dimensional reference images. As a learning system, an adaptive fuzzy inference neural network (AFINN) is used, which can perform knowledge processing. The AFINN automatically constructs the inference model, such as the selection of the necessary input information, and automatically acquires the inference rules through learning. Consequently, by applying the AFINN to the image recognition problem, highly versatile object recognition is realized without requiring explicit knowledge for recognition. The proposed system can be applied to a wide range of problems, since diversified objects can be recognized by simply modifying the registered reference images. In this paper, the method is applied to a sample problem, consisting of an image of the laboratory containing various objects, such as computers, monitors, and chairs, and it is verified that high recognition accuracy can be realized. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(3): 69–79, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20269 [ABSTRACT FROM AUTHOR] more...

A new method of extracting various types of human knowledge from scenery images in linguistic form and labeling the various regions of unknown images in an effective manner is proposed. In this system, partitioning of the input/output space can be performed in a self-organizing manner. Rule extraction is performed in parallel and automatically by using a knowledge extraction network (KEN). Depending on the type of image to be learned, two-phase processing including a self-organization phase and an LMS learning phase is performed by several KENs. The overall network output is then unified and knowledge extraction and region recognition are performed linguistically. Software that performs a series of such operations has been implemented and it has been confirmed that knowledge extraction and better image recognition can be performed linguistically even if human knowledge is not provided. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(3): 82–90, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10138 [ABSTRACT FROM AUTHOR] more...

Purpose: This study presents a novel application of deep learning to enhance the accuracy of left-right orientation identification in anatomical brain MRI scans. Left-right orientation misidentification in brain MRIs presents significant challenges due to several factors, including metadata loss or ambiguity, which often occurs during the de-identification of medical images for research, conversion between image formats, software operations that strip or overwrite metadata, and the use of older imaging systems that stored orientation differently., Method: A three-dimensional convolutional neural network model was trained using 350 MRIs and evaluated on the basis of eight distinct brain MRI databases, totaling 3056 MRIs, to assess its performance across various conditions, including neurodegenerative diseases., Finding: The proposed deep-learning framework demonstrated a 99.8% accuracy in identifying the left-right orientation, thus, addressing challenges associated with the loss of orientation metadata. GradCAM was used to visualize areas of the brain where the model focused, demonstrating the importance of the right planum temporale and surrounding areas in judging left-right orientation. The planum temporale is known to exhibit notable left-right asymmetry related to language functions, underscoring the biological validity of the model. The half of the four left-right misidentified MRIs involved notable brain feature variations, such as a large arachnoidal cyst adjacent to the temporal lobe or ventricular asymmetry, indicating areas for further investigation., Conclusion: This approach offers a potential solution to the persistent issue of left-right misorientation in brain MRIs and supports the reliability of neuroscientific research by ensuring accurate data interpretation., (© 2025 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.) more...

Content-based image retrieval (CBIR) is a technology designed to retrieve images from a database based on visual features. While the CBIR is highly desired, it has not been applied to clinical neuroradiology, because clinically relevant neuroradiological features are swamped by a huge number of noisy and unrelated voxel information. Thus, effective dimension reduction is the key to successful CBIR. We propose a novel dimensional compression method based on 3D convolutional autoencoders (3D-CAE), which was applied to the ADNI2 3D brain MRI dataset. Our method succeeded in compressing 5 million voxel information to only 150 dimensions, while preserving clinically relevant neuroradiological features. The RMSE per voxel was as low as 8.4%, suggesting a promise of our method toward the application to the CBIR. more...

The objective of this study was to evaluate the relation between age and dermatoscopic features of acral nevi. We evaluated 159 dermatoscopic images of melanocytic nevi from 146 individuals filed at the Dermatoscopy Outpatient Clinic of Tokyo Women's Medical University Medical Center East between April 2006 and March 2009. All images of melanocytic lesions on acral volar skin that showed a clear-cut dermatoscopic pattern of an acral nevus at the time of initial observation were included. The dermatoscopic patterns of all images were retrospectively examined in a blinded fashion according to the standard dermatoscopic classification criteria for acral melanocytic nevi. Images were classified using 15 structural variants of the parallel furrow pattern. These variants were then re-classified into two groups; the "single" line group and "double" line group. Patients of the double line group (age, 25.5 years) were significantly younger than those of the single line group (32.4 years). There was no significant difference in the age-related predominance between the solid line patterns and dotted line patterns. There was a significant age difference between patients with nevi showing the crista dotted pattern (mean age 24.9 years) and patients with nevi without the crista dotted pattern (mean age 34.6 years). We conclude that the double line variant of the parallel furrow pattern and crista dotted pattern, which probably correspond to the congenital type acral nevus, tend to be more common in young patients. more...

In stereotactic radiosurgery we can irradiate a targeted volume precisely with a narrow high-energy x-ray beam, and thus the motion of a targeted area may cause side effects to normal organs. This paper describes our motion detection system with three USB cameras. To reduce the effect of change in illuminance in a tracking area we used an infrared light and USB cameras that were sensitive to the infrared light. The motion detection of a patient was performed by tracking his/her ears and nose with three USB cameras, where pattern matching between a predefined template image for each view and acquired images was done by an exhaustive search method with a general-purpose computing on a graphics processing unit (GPGPU). The results of the experiments showed that the measurement accuracy of our system was less than 0.7 mm, amounting to less than half of that of our previous system. more...

A relationship between autonomic nerves activity and depression or Alzheimer's disease has been reported. The quantification of autonomic nerves is expected to serve as a tool for quantifying the of severity of the disease or for early detection. Video-oculography is known as a non-invasive and reliable procedure of measurement of pupil response and is used in clinical practice. However, measuring the transition of pupil areas accurately is often difficult due to eyelid overlap, effects of blinking, eyelashes etc. Current video-oculography only performs thresholding to split pupil area and backgrounds and therefore sometimes has difficult in measuring accurate transitions of pupil reflex. In this study, we developed a robust and accurate method to measure the transition of pupil size. The proposed method introduces an interpolation process using an active contour model and ellipse estimation with selection of reliable contour points and attains robust measurement of pupil area against the abovementioned difficulties. We confirmed our method achieved an extraction accuracy of 98.3 % in precision and 98.9% in recall in average on the tested a total of 8,518 image frames from 30 movies. more...

Accurate identification of lesion borders is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. Snakes have been used for segmenting a variety of medical imagery including dermoscopy, however, due to the compromise of internal and external energy forces they can lead to under- or over-segmentation problems. In this paper, we introduce a mean shift based gradient vector flow (GVF) snake algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images. more...

In this paper, we present a classification method of dermoscopy images between melanocytic skin lesions (MSLs) and non-melanocytic skin lesions (NoMSLs). The motivation of this research is to develop a pre-processor of an automated melanoma screening system. Since NoMSLs have a wide variety of shapes and their border is often ambiguous, we developed a new tumor area extraction algorithm to account for these difficulties. We confirmed that this algorithm is capable of handling different dermoscopy images not only those of NoMSLs but also MSLs as well. We determined the tumor area from the image using this new algorithm, calculated a total 428 features from each image, and built a linear classifier. We found only two image features, "the skewness of bright region in the tumor along its major axis" and "the difference between the average intensity in the peripheral part of the tumor and that in the normal skin area using the blue channel" were very efficient at classifying NoMSLs and MSLs. The detection accuracy of MSLs by our classifier using only the above mentioned image feature has a sensitivity of 98.0% and a specificity of 86.6% in a set of 107 non-melanocytic and 548 melanocytic dermoscopy images using a cross-validation test. more...

Computer aided diagnosis of dermoscopy images has shown great promise in developing a quantitative, objective way of classifying skin lesions. An important step in the classification process is the lesion segmentation. Many papers have been successful at segmenting melanocytic skin lesions (MSLs) but few have focused on non-melanocytic skin lesions (NoMSLs), since the wide variety of lesions makes accurate segmentation difficult. We developed an automatic segmentation program for the border detection of skin lesions. We tested our method on a set of 107 non-melanocytic lesions and on a set of 319 melanocytic lesions. Our method achieved precision/recall scores of 84.5% and 88.5% for NoMSLs, achieving higher scores than two previously published methods. Our method also achieved precision/recall scores of 93.9% and 93.8% for MSLs which was competitive or better than the two other methods. Therefore, we conclude that our approach is an accurate segmentation method for both melanocytic and non-melanocytic lesions. more...

Background: Myocardial perfusion single-photon emission computed tomography (SPECT) has been used for risk stratification before non-cardiac surgery. However, few authors have used mathematical models for evaluating the likelihood of perioperative cardiac events., Methods and Results: This retrospective cohort study collected data of 1,351 patients referred for SPECT before non-cardiac surgery. We generated binary classifiers using support vector machine (SVM) and conventional linear models for predicting perioperative cardiac events. We used clinical and surgical risk, and SPECT findings as input data, and the occurrence of all and hard cardiac events as output data. The area under the receiver-operating characteristic curve (AUC) was calculated for assessing the prediction accuracy. The AUC values were 0.884 and 0.748 in the SVM and linear models, respectively in predicting all cardiac events with clinical and surgical risk, and SPECT variables. The values were 0.861 (SVM) and 0.677 (linear) when not using SPECT data as input. In hard events, the AUC values were 0.892 (SVM) and 0.864 (linear) with SPECT, and 0.867 (SVM) and 0.768 (linear) without SPECT., Conclusion: The SVM was superior to the linear model in risk stratification. We also found an incremental prognostic value of SPECT results over information about clinical and surgical risk. more...

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. Border detection is often the first step in the automated analysis of dermoscopy images. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground-truth. In contrast to previous work, we utilize an objective measure, the Normalized Probabilistic Rand Index, which takes into account the variations in the ground-truth images. The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by commonly used measures. more...

In this paper, we present an Internet-based melanoma screening system that newly supports acral volar lesions. A half of Asian melanomas are from these areas and they show completely different appearance from other lesions. Our screening system is accessible from all over the world and diagnoses dermoscopy images within 3-5 sec based on a neural network classifier for non-acral lesions or newly integrated linear classifier for acral volar lesions. Our system achieves a sensitivity of 85.9% and a specificity of 86.0% on a set of 1258 non-acral dermoscopy images and a sensitivity of 93.3% and a specificity of 91.1% on a set of 199 acral volar dermoscopy images using a leave-one-out cross-validation. more...

The aims of this study were to provide a quantitative assessment of the tumour area extracted by dermatologists and to evaluate computer-based methods from dermoscopy images for refining a computer-based melanoma diagnostic system. Dermoscopic images of 188 Clark naevi, 56 Reed naevi and 75 melanomas were examined. Five dermatologists manually drew the border of each lesion with a tablet computer. The inter-observer variability was evaluated and the standard tumour area (STA) for each dermoscopy image was defined. Manual extractions by 10 non-medical individuals and by two computer-based methods were evaluated with STA-based assessment criteria: precision and recall. Our new computer-based method introduced the region-growing approach in order to yield results close to those obtained by dermatologists. The effectiveness of our extraction method with regard to diagnostic accuracy was evaluated. Two linear classifiers were built using the results of conventional and new computer-based tumour area extraction methods. The final diagnostic accuracy was evaluated by drawing the receiver operating curve (ROC) of each classifier, and the area under each ROC was evaluated. The standard deviations of the tumour area extracted by five dermatologists and 10 non-medical individuals were 8.9% and 10.7%, respectively. After assessment of the extraction results by dermatologists, the STA was defined as the area that was selected by more than two dermatologists. Dermatologists selected the melanoma area with statistically smaller divergence than that of Clark naevus or Reed naevus (P = 0.05). By contrast, non-medical individuals did not show this difference. Our new computer-based extraction algorithm showed superior performance (precision, 94.1%; recall, 95.3%) to the conventional thresholding method (precision, 99.5%; recall, 87.6%). These results indicate that our new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted. With this refinement, the area under the ROC increased from 0.795 to 0.875 and the diagnostic accuracy showed an increase of approximately 20% in specificity when the sensitivity was 80%. It can be concluded that our computer-based tumour extraction algorithm extracted almost the same area as that obtained by dermatologists and provided improved computer-based diagnostic accuracy. more...