Development of an integrated computerized scheme for metaphase chromosome image analysis: a robustness experiment

The authors developed an integrated computer-aided detection (CAD) scheme for detecting and classifying metaphase chromosomes as well as assessing its performance and robustness. This scheme includes an automatic metaphase-finding module and a karyotyping module and it was applied to a testing database with 200 digital microscopic images. The automatic metaphase-finding module detects analyzable metaphase cells using a feature-based artificial neural network (ANN). The ANN-generated outputs are analyzed by a receiver operating characteristics (ROC) method and an area under the ROC curve is 0.966. Then, the automatic karyotyping module classifies individual chromosomes of this cell into 24 types. In this module, a two-layer decision tree-based classifier with eight ANNs established in its connection nodes was optimized by a genetic algorithm. Chromosomes are first classified into seven groups by the ANN in the first layer. The chromosomes in these groups are then separately classified by seven ANNs into 24 types in the second layer. The classification accuracy is 94.5% in the first layer. Six ANNs achieved the accuracy above 95% and only one had lessened performance (80.6%) in the second layer. The overall classification accuracy is 91.5% as compared to 86.7% in the previous study using two independent datasets randomly acquired from our genetic laboratory. The results demonstrate that our automated scheme achieves high and robust performance in identification and classification of metaphase chromosomes.

In this study, we examine a new case based approach to computer-aided detection (CAD) schemes of screening mammograms based on the computation of many global bilateral asymmetry and mammographic density image based features. The current commercialized CAD schemes have high false positive detection rates, which also have high positive lesion detection correlations with radiologists. Thus, we developed a new global image feature based CAD scheme that can cue the warning sign on the cases with high risk of being positive using an extended feature set of 158 mammographic density and texture based features computed on all four craniocaudal (CC) and mediolateral oblique (MLO) view images. We utilized a modified fast and accurate sequential floating forward selection feature selection algorithm and applied selected features to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based classification score. We tested our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative). The area under the receiver operating characteristic curve was AUC = 0.742±0.016. Odds ratios increased from 1 to 15.43 as the CAD-generated case based detection scores increased. The results of the study show that useful information can be derived from the global mammographic density image based features that can be examined further as a new paradigm/approach of CAD for screening mammograms.

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms

The amplification status of human epidermal growth factor receptors 2 (HER2) genes is strongly associated with clinical outcome in patients with breast cancer. The American Society of Clinical Oncology Tumor Marker Guidelines Panel has recommended routine testing of HER2 status on all newly diagnosed metastatic breast cancers since 2001. Although fluorescent in situ hybridization (FISH) technology provides superior accuracy as compared with other approaches, current manual FISH analysis methods are somewhat subjective, tedious, and may introduce interreader variability. The goal of this preliminary study is to develop and test a computer-aided detection (CAD) scheme to assess HER2 status using FISH images. Forty FISH images were selected for this study from our genetic laboratory. The CAD scheme first applies an adaptive, iterative threshold method followed by a labeling algorithm to segment cells of possible interest. A set of classification rules is then used to identify analyzable interphase cells and discard nonanalyzable cells due to cell overlapping and/or other image staining debris (or artifacts). The scheme then maps the detected analyzable cells onto two other gray scale images corresponding to the red and green color of the original image followed by application of a raster scan and labeling algorithms to separately detect the HER-2/neu ("red") and CEP17 ("green") FISH signals. A simple distance based criterion is applied to detect and merge split FISH signals within each cell. The CAD scheme computes the ratio between independent "red" and "green" FISH signals of all analyzable cells identified on an image. If the ratio is ≥ 2.0, the FISH image is assumed to have been acquired from a HER2+ case; otherwise, the FISH image is assumed to have been acquired from HER2- case. When we applied the CAD scheme to the testing dataset, the average computed HER2 amplification ratios were 1.06±0.25 and 2.53±0.81 for HER2- and HER2+ samples, respectively. The results show that the CAD scheme has the ability to automatically detect HER2 status using FISH images. The success of CAD-guided FISH image analysis could result in a more objective, consistent, and efficient approach in determining HER2 status of breast cancers.

Background. The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented. Methods. In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method. Results. The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity. Conclusion. This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886±0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

Fully Automated Three-Dimensional Detection of Polyps in Fecal-Tagging CT Colonography

It is difficult to detect sentinel lymph nodes (SLNs) around an injection point of radiopharmaceuticals mapped in lymphoscintigrams. The purpose of this study was to develop a computer-aided detection (CAD) scheme for SLNs by a subtraction technique using the symmetrical property in the mapped injection point. Our database consisted of 78 lymphoscintigrams with 86 SLNs. In our CAD scheme, the mapped injection point of radiopharmaceuticals was first segmented from the lymphoscintigram using a gray-level thresholding technique. Lymphoscintigram was then divided into four regions by vertical and horizontal straight lines through the center of the segmented injection point. One of the four divided regions was defined as the target region. The correlation coefficients based on pixel values were calculated between the target region and each of the other three regions. The region with the highest correlation coefficient among three regions was selected as the similar region to the target region. The values of pixels on the target region were subtracted by the values of the corresponding pixels on the similar region. This procedure was repeated until every divided region had been used as target region. SLNs were segmented by applying a gray-level thresholding technique to the subtracted image. With our CAD scheme, sensitivity and the number of false positives were 95.3% (82/86) and 2.51 per image, respectively. Our CAD scheme achieved a high level of detection accuracy, and would have a great potential in assisting physicians to detect SLNs in lymphoscintigrams.

Most of the computer systems developed for the detection or diagnosis of breast lesions share some common steps. Indeed, the typical phases needed for a computer-aided detection (CAD) program are shown in Fig. 7.1. The first operations aim to reduce the noise of the digital mammogram and to isolate suspicious lesions. After that, a feature extraction step is usually carried out. Automatic feature extraction procedures utilize image analysis techniques for the computation of feature vectors characteristic of the segmented lesions. Typically, the lesions are then identified, or characterized, by exploiting the information of the calculated features. Then, a classification phase is performed, where the extracted features are provided as inputs to a classifier. In detection schemes, this phase is known as false-positive reduction. Here, it is necessary to set up a classifier that, hopefully, maintains all the true detected signals, and at the same time, rejects almost all the false positive signals. For diagnosis schemes, this phase aims to characterize the lesion as benign or malignant, on the basis of its features. Several types of classifiers have been investigated, such as artificial neural networks, linear discriminant analysis, and decision trees. Most of the techniques are supervised methods. Recently, a new family of classifiers has appeared in CAD schemes: the support vector machine (SVM). SVMs have been introduced as a technique that relies on statistical learning theory (SLT). Whereas other techniques, e.g., multilayer perceptrons (MLPs), are based on the minimization of the empirical risk, which is the minimization of the number of misclassified vectors of the training set, where SVMs minimize a function that is the sum of two terms. The first term is the empirical risk, the second term (confidence term) controls the ability of the machine to learn any training set without error. SVMs are attracting increasing attention because they rely on a solid statistical foundation and appear to perform quite effectively in many different applications. After training, the separating surface is expressed as a certain linear combination of a given kernel function centered at some of the data vectors (named support vectors). All the remaining vectors of the training set are effectively discarded and the classification of new vectors is obtained solely in terms of the support vectors (SVs). Usually, the smaller the percentage of SVs, the better the generalization of the machine. The interpretative key lies in the fact that, for SVMs, the modeling of the classes is based on the properties of the example vectors at the boundary edge between the two classes. Indeed, what the SVM adds to the other classifiers is a better check of the boundary cases, the ones where it is more difficult to decide whether they belong to one class or to the other. Since it has a sample of examples representing the distribution at the edges of the two classes, the SVM uses these examples for drawing a boundary map between the classes. In this way, much fewer examples are required for carrying out generalizations than would be necessary if it was required to model the entire distribution of the vectors of the class in order to draw out the mean properties. SVMs have been used both in detection and in diagnosis tasks. In both cases, the SVM acts as a classifier that tends to separate two classes of objects: lesions and nonlesions in detection schemes, and benign and malignant (or normal and abnormal) in diagnostic programs. Several studies have demonstrated that in most conditions, the SVM classifier, thanks to its properties, outperforms other type of classifiers.

Content-based image retrieval approach was used in our computer-aided detection (CAD) schemes for breast cancer detection with mammography. In this study, we assessed CAD performance and reliability using a reference database including 1500 positive (breast mass) regions of interest (ROIs) and 1500 normal ROIs. To test the relationship between CAD performance and the similarity level between the queried ROI and the retrieved ROIs, we applied a set of similarity thresholds to the retrieved similar ROIs selected by the CAD schemes for all queried suspicious regions, and used only the ROIs that were above the threshold for assessing CAD performance at each threshold level. Using the leave-one-out testing method, we computed areas under receiver operating characteristic (ROC) curves (AZ) to assess CAD performance. The experimental results showed that as threshold increase, (1) less true positive ROIs can be referenced in the database than normal ROIs and (2) the AZ value was monotonically increased from 0.854±0.004 to 0.932±0.016. This study suggests that (1) in order to more accurately detect and diagnose subtle masses, a large and diverse database is required, and (2) assessing the reliability of the decision scores based on the similarity measurement is important in application of the CBIR-based CAD schemes when the limited database is used.

Prostate cancer is one of the most frequently diagnosed neoplasy and its presence can only be confirmed by biopsy. Due to the high number of false positives, Computer Aided Detection (CAD) systems can be used to reduce the number of cores requested for an accurate diagnosis. This work proposes a CAD procedure for cancer detection in Ultrasound images based on a learning scheme which exploits a novel semi-supervised learning (SSL) algorithm for reducing data collection effort and avoiding collected data wasting. The ground truth database comprises the RFsignals acquired during biopsies and the corresponding tissue samples histopathological outcome. A comparison to a state-of-art CAD scheme based on supervised learning demonstrates the effectiveness of the proposed SSL procedure at enhancing CAD performance. Experiments on ground truth images from biopsy findings show that the proposed CAD scheme is effective at improving the efficiency of the biopsy protocol.

Computerized detection of masses from digitized mammograms: Comparison of single-image segmentation and bilateral-image subtraction

The purpose of this study is to reveal how the performance of lung nodule segmentation algorithm impacts the performance of lung nodule detection, and to provide guidelines for choosing an appropriate segmentation algorithm with appropriate parameters in a computer-aided detection (CAD) scheme. The database consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter from the standard CT lung nodule database created by the Lung Image Database Consortium. The initial nodule candidates were identified as those with strong response to a selective nodule enhancement filter. A uniform viewpoint reformation technique was applied to a three-dimensional nodule candidate to generate 24 two-dimensional (2D) reformatted images, which would be used to effectively distinguish between true nodules and false positives. Six different algorithms were employed to segment the initial nodule candidates in the 2D reformatted images. Finally, 2D features from the segmented areas in the 24 reformatted images were determined, selected, and classified for removal of false positives. Therefore, there were six similar CAD schemes, in which only the segmentation algorithms were different. The six segmentation algorithms included the fixed thresholding (FT), Otsu thresholding (OTSU), fuzzy C-means (FCM), Gaussian mixture model (GMM), Chan and Vese model (CV), and local binary fitting (LBF). The mean Jaccard index and the mean absolute distance (Dmean) were employed to evaluate the performance of segmentation algorithms, and the number of false positives at a fixed sensitivity was employed to evaluate the performance of the CAD schemes. For the segmentation algorithms of FT, OTSU, FCM, GMM, CV, and LBF, the highest mean Jaccard index between the segmented nodule and the ground truth were 0.601, 0.586, 0.588, 0.563, 0.543, and 0.553, respectively, and the corresponding Dmean were 1.74, 1.80, 2.32, 2.80, 3.48, and 3.18 pixels, respectively. With these segmentation results of the six segmentation algorithms, the six CAD schemes reported 4.4, 8.8, 3.4, 9.2, 13.6, and 10.4 false positives per CT scan at a sensitivity of 80%. When multiple algorithms are available for segmenting nodule candidates in a CAD scheme, the "optimal" segmentation algorithm did not necessarily lead to the "optimal" CAD detection performance.

Breast cancer is the first cause of death among women in Europe. Many studies have shown that the early diagnosis is the most efficient way to fight this disease. With this in mind, Computer Aided Detection (CAD) systems are being developed to help radiologists working with mammography to assess correct diagnosis. The main goal of these systems is to direct the radiologist's attention to suspicious areas. In this paper we present the software architecture of GNN-CAD (Gabor Neural Network CAD), a CAD system for the detection and classification of breast calcifications. Our approach is based on the multiresolution space-frequency scheme to the representation of the image known as Gabor Transform. The Gabor Neural Network CAD (GNN-CAD) system works in several steps. Digitized mammograms are first preprocessed, then, the image spectral and spatial features extracted by a bank of Gabor filters at different spatial and spatial-frequency resolutions are used to train a three layers feed-forward Artificial Neural Network (ANN) to discriminate between normal pixels and pixel belonging to isolated and clustered microcalcifications. The microcalcifications thus detected are grouped in clusters and classified.

Computer-aided detection (CADe) and computer-aided diagnosis (CADx) are emerging technologies to help radiologists interpret medical images. In screening mammography, CADe can help radiologists avoid overlooking a cancer, while CADx can help radiologists decide whether a biopsy is warranted when reading a diagnostic mammogram. Even though there is much commonality in the techniques used in CADe and CADx algorithms, there are important differences in the input data and in the output of the algorithms. In particular, CADe outputs the location of potential cancers, while CADx outputs the likelihood that a known lesion is malignant. These differences affect the metrics used to evaluate their performance. Commercial CADe systems have been developed and clinical studies of CADe have indicated the ability to increase radiologists' sensitivity by approximately 10% with a comparable increase in the recall rate. Commercial CADx systems do not exist till date, but observer study results are very compelling. CADe and CADx schemes continue to evolve in terms of accuracy and user interface. It is expected that CADe and eventually CADx will play an increasingly important role in breast imaging in the future.