Recently, as chemotherapy has advanced, it is important to accurately diagnosis the histological type (adenocarcinoma, squamous cell carcinoma and small cell carcinoma). In previous study, automated classification method for lung cancers in cytological images using a deep convolutional neural network (DCNN) was proposed. However, its classification accuracy is approximately 70%, therefore improvement in accuracy is required. In this study, we focus on liquid-based cytology images and clinical record. In this study, we aimed to improve the classification accuracy of lung cancer type by combining cytological images and electronic medical records. We aimed to develop of classification method of lung tumor type by combining cytological images and clinical record. First, the cytological images were collected. The original microscopic images were first cropped to obtain images with resolution 256 × 256 pixels. And then, we collected personal clinical data (age, gender, smoking status, laboratory test values, tumor markers and so on) corresponding to cytological images. Next, image features were extracted from cytological images using VGG-16 model pretrained on the ImageNet dataset. 4096 features before the fully connected layer were extracted. Then, these features were reduced dimensions by PCA. Image features obtained from the DCNN and clinical data corresponding to cytological images were given to the classifier. Finally, classification result of 3 histological categories was obtained. Evaluation results showed that classification by combining cytological images and clinical record improved classification accuracy than by cytological images alone. These results indicate that the proposed method may be useful for histological classification of lung tumor.
Breast cancer incidence tends to rise globally and the mortality rate for breast cancer is increasing in Japan. There are various screening modalities for breast cancer, and MRI examinations with high detection rate are used for high-risk groups, which are genetically prone to develop breast cancer. In the breast MRI examination, unenhanced T1 and T2 weighted images shows no significant difference in signal value between tumor and normal tissue. Therefore, tumors are identified with use of contrast enhanced kinetic curve obtained by dynamic scan using contrast agent. Some computer aided diagnosis methods using dynamic contrast enhanced MR images also have been proposed. However, contrast agent produces the allergic reaction in rare case; it should not be used for screening examinees. Here, MRI provides the anatomical and functional information by using various sequences without contrast agents. According to the reports, this information can discriminate between tumor and normal tissue. In this study, we analyzed unenhanced MR images by using plural sequences and developed an automated method for the detection of tumors. First, we extracted the breast region from the T1-weighted image semi-automatically. Next, using the threshold determined by considering the signal intensities of tumor and normal tissue, a thresholding method was applied for diffusion-weighted image to extract the first candidate regions. After labeling processing, the breast region removes outside candidates from Initial candidates. Then false positives are reduced by the rule-based classifier. Finally, we examined the remaining candidates as possible tumor regions. We applied the proposed method to 54 cases of MR images and evaluated its usefulness. As a result, the detection sensitivity was 71.9% and the abnormal regions were clearly detected. These results indicate that the proposed method may be useful for tumor detection in unenhanced breast MR images.
In a previous study, we developed a hybrid tumor detection method that used both computed tomography (CT) and positron emission tomography (PET) images. However, similar to existing computer-aided detection (CAD) schemes, it was difficult to detect low-contrast lesions that touch to the normal organs such as the chest wall or blood vessels in the lung. In the current study, we proposed a novel lung tumor detection method that uses active contour filters to detect the nodules deemed "difficult" in previous CAD schemes. The proposed scheme detects lung tumors using both CT and PET images. As for the detection in CT images, the massive region was first enhanced using an active contour filter (ACF), which is a type of contrast enhancement filter that has a deformable kernel shape. The kernel shape involves closed curves that are connected by several nodes that move iteratively in order to enclose the massive region. The final output of ACF is the difference between the maximum pixel value on the deformable kernel, and pixel value on the center of the filter kernel. Subsequently, the PET images were binarized to detect the regions of increased uptake. The results were integrated, followed by the false positive reduction using 21 characteristic features and three support vector machines. In the experiment, we evaluated the proposed method using 100 PET/CT images. More than half of nodules missed using previous methods were accurately detected. The results indicate that our method may be useful for the detection of lung tumors using PET/CT images.
Micro-optical computed tomography (MOCT) is a method for performing image reconstruction using microscopic images to obtain tomographic images of small samples. Compared with conventional observation methods, it offers the possibility to obtain tomograpic images without distortion, and create three-dimensional images. However, MOCT system which developed previously outputs monochrome images, while useful color information could not be obtained from the analysis of the sample. Therefore, we focused on the features that simplify the wavelength measurement of visible light, and developed a color MOCT system that can obtain color tomographic images. In this study, we acquired tomographic images of phantom and biological samples, and evaluated its usefulness. In this system, a digital single-lens reflex camera was used as a detector that was connected to a stereoscopic microscope, and projection images were obtained by rotating the sample. The sample was fixed in the test tube by carrageenan. The projection images were obtained from various projection angles followed by decomposing the R, G and B components. Subsequently, we performed image reconstruction for each component using filtered back projection. Finally, color tomographic image was obtained by combining the three-color component images. In the experiments, we scanned a color phantom and biological samples and evaluated the color and shape reproducibility. As a result, it was found that the color and shape of the tomographic images were similar to those of the samples. These results indicate that the proposed system may be useful to obtain the three-dimensional color structure of biological samples.
The advantage of X-ray phase imaging is its ability to obtain information on soft tissues, which is difficult
using conventional X-ray imaging. Moreover, a sharp X-ray image can be obtained from the edge effect
resulting from phase contrast. Digital tomosynthesis is an imaging technique used to reconstruct multiple
planes in a single scan. In this study, we developed an experimental system that combines the
phase-contrast and digital tomosynthesis techniques. Our experimental system consists of a
transmission-type micro-focus X-ray source (minimum focus size: 1 μm). We also introduced an indirect
conversion-type flat panel detector (pixel pitch: 50 μm, matrix size: 2366 × 2368) as an imaging device.
The sample is placed on a computer-controlled rotation table, and projection images are captured from
various angles. The images are then reconstructed using the filtered back projection method. In the
experiments, a tomosynthesis image of an acrylic phantom was obtained at a tube voltage of 40 kV and at
a maximum projection angle of ±20°. To evaluate the edge enhancement effect by phase contrast, the
resolution, degree of edge enhancement, and image contrast were measured using the acrylic phantom. A
good edge enhancement effect was confirmed under the specified conditions. Furthermore, we compared
to the shape between the projection image and the tomosynthesis image and found that the tomosynthesis
image showed high shape reproducibility compared to the conventional projection image. These results
indicate that phase-contrast digital tomosynthesis may be useful for the three-dimensional imaging of
low-contrast material.
Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the
architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting
architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.
In this study, an automated scheme for detecting pulmonary nodules in PET/CT images has
been proposed using combined detection and hybrid false-positive (FP) reduction techniques.
The initial nodule candidates were detected separately from CT and PET images. FPs were
then eliminated in the initial candidates by using support vector machine with characteristic
values obtained from CT and PET images. In the experiment, we evaluated proposed method
using 105 cases of PET/CT images that were obtained in the cancer-screening program. We
evaluated true positive fraction (TPF) and FP / case. As a result, TPFs of CT and PET
detections were 0.76 and 0.44, respectively. However, by integrating the both results, TPF was
reached to 0.82 with 5.14 FPs/case. These results indicate that our method may be of practical
use for the detection of pulmonary nodules using PET/CT images.
Lung cancer is the leading cause of death among male in the world. PET/CT is useful for the detection of
early lung cancer since it is an imaging technique that has functional and anatomical information. However,
radiologist has to examine using the large number of images. Therefore reduction of radiologist's
load is strongly desired. In this study, hybrid CAD scheme has been proposed to detect lung nodule in
PET/CT images. Proposed method detects the lung nodule from both CT and PET images. As for the detection
in CT images, solitary nodules are detected using Cylindrical Filter that we developed. PET images
are binarized based on standard uptake value (SUV); highly uptake regions are detected. FP reduction
is performed using seven characteristic features and Support Vector Machine. Finally by integrating
these results, candidate regions are obtained. In the experiment, we evaluated proposed method using 50
cases of PET/CT images obtained for the cancer-screening program. We evaluated true-positive fraction
(TPF) and the number of false positives / case (FPs/case). As a result, TPFs for CT and PET were 0.67
and 0.38, respectively. By integrating the both results, TPF was improved to 0.80. These results indicate
that our method may be useful for the lung cancer detection using PET/CT images.
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