OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain.

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.

Significant Dimension Reduction of 3D Brain MRI using 3D Convolutional Autoencoders.

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.

Four-class classification of skin lesions with task decomposition strategy.

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.

Age-related prevalence of dermatoscopic patterns of acral melanocytic nevi.

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.

Automated reconstruction algorithm for identification of 3D architectures of cribriform ductal carcinoma in situ.

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.

Lesion border detection in dermoscopy images using ensembles of thresholding methods.

Background: 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, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis.

Robust video-oculography for non-invasive autonomic nerve quantification.

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.

Feasibility evaluation of a motion detection system with face images for stereotactic radiosurgery.

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.

Three-phase general border detection method for dermoscopy images using non-uniform illumination correction.

Background: 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 lesion segmentation. Many studies have been successful in segmenting melanocytic skin lesions (MSLs), but few have focused on non-melanocytic skin lesions (NoMSLs), as the wide variety of lesions makes accurate segmentation difficult.

Skin lesion segmentation using an improved snake model.

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.

Classification of melanocytic skin lesions from non-melanocytic lesions.

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.

Development of a novel border detection method for melanocytic and non-melanocytic dermoscopy images.

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.

Colour and contrast enhancement for improved skin lesion segmentation.

Accurate extraction of lesion borders is a critical step in analysing dermoscopic skin lesion images. In this paper, we consider the problems of poor contrast and lack of colour calibration which are often encountered when analysing dermoscopy images. Different illumination or different devices will lead to different image colours of the same lesion and hence to difficulties in the segmentation stage.

Automated color calibration method for dermoscopy images.

Accurate color information in dermoscopy images is very important for melanoma diagnosis since inappropriate white balance or brightness in the images adversely affects the diagnostic performance. In this paper, we present an automated color calibration method for dermoscopy images of skin lesions. On a set of 319 dermoscopy images, we develop color calibration filters based on the HSV color system.

An improved objective evaluation measure for border detection in dermoscopy images.

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Owing to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.

Approximate lesion localization in dermoscopy images.

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Because of the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis.

An Internet-based melanoma screening system with acral volar lesion support.

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.

Objective evaluation of methods for border detection in dermoscopy images.

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.

Border detection in dermoscopy images using statistical region merging.

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.

Lesion border detection in dermoscopy images.

Background: 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, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.

Application of support vector machine classifiers to preoperative risk stratification with myocardial perfusion scintigraphy.

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.

Automatic detection of blue-white veil and related structures in dermoscopy images.

Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images.

An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm.

In this paper, we present an Internet-based melanoma screening system. Our web server is accessible from all over the world and performs the following procedures when a remote user uploads a dermoscopy image: separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, calculates a total of 428 features for the characterization of the tumor, classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis. Our system achieves a sensitivity of 85.

Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin.

We describe a fully automated system for the classification of acral volar melanomas. We used a total of 213 acral dermoscopy images (176 nevi and 37 melanomas). Our automatic tumor area extraction algorithm successfully extracted the tumor in 199 cases (169 nevi and 30 melanomas), and we developed a diagnostic classifier using these images.