• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.700-704
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• 2009

In this paper, we propose a method for combining MR image with histopathology image of the prostate using image correction and multi-stage registration. Our method consists of four steps. First, the intensity of prostate bleeding area on T2-weighted MR image is substituted for that on T1-weighted MR image. And two or four tissue sections of the prostate in histopathology image are combined to produce a single prostate image by manual stitching. Second, rigid registration is performed to find the affine transformations that to optimize mutual information between MR and histopathology images. Third, the result of affine registration is deformed by the TPS warping. Finally, aligned images are visualized by the intensity intermixing. Experimental results show that the prostate tumor lesion can be properly located and clearly visualized within MR images for tissue characterization comparison and that the registration error between T2-weighted MR and histopathology image was 0.0815mm.

• Applied Microscopy
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• v.51
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.127-135
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• 2023

Accurate segmentation of histopathology whole slide images (WSIs) is a crucial task for disease diagnosis and treatment planning. However, conventional automated segmentation algorithms may not always be applicable to WSI segmentation due to their large size and variations in tissue appearance, staining, and imaging conditions. Recent advances in interactive segmentation, which combines human expertise with algorithms, have shown promise to improve efficiency and accuracy in WSI segmentation but also presented us with challenging issues. In this paper, we propose a novel interactive segmentation method, ZoomISEG, that leverages multi-resolution WSIs. We demonstrate the efficacy and performance of the proposed method via comparison with conventional single-scale methods and an ablation study. The results confirm that the proposed method can reduce human interaction while achieving accuracy comparable to that of the brute-force approach using the highest-resolution data.

• Investigative Magnetic Resonance Imaging
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• v.22 no.3
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• pp.194-199
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• 2018

We present image findings, especially rare MRI of a primary breast angiosarcoma with its histopathology, and also analyze the relevant medical literature reports in terms of the MRI findings. As our patient had unique features of a primary breast angiosarcoma, this case could be very helpful for future diagnosis of this rare breast malignancy by MRI.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.136-139
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• 2016

Clear cell sarcoma is rare and difficult to diagnose. Herein, we present a case of clear cell sarcoma in the dorsum of the wrist with MRI findings, including diffusion-weighted imaging, and histopathologic correlation, which was initially diagnosed as giant cell tumor of tendon sheath.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.539-548
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• 2020

Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.21 no.4
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• pp.898-906
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• 2007

This study was to evaluate the effects of oral administration of Processed Sulphur on Hepatotoxicity. Processed Sulphur was administered orally to rats for 28 days. We measured the body and liver weight index, heamtological and biomedical parameters. We also observed the histopathological changes of liver in rats. No significant differences in body weight, liver weight index, heamtological and biomedical parameters and histopathological changes of hepatocyte between control and Processed Sulphur fed group were found. Our data indicate that hepatotoxicity was not caused by oral medication of Processed Sulphur up to 60mg/200g/day for 28 days in rats. Therefore, Processed Sulphur appears to be safe and non-toxic in these studies and a no-observed adverse effect level (NOAEL) in rats is established at 60mg/200g/day. The data could provide satisfactory preclinical evidence of safety to launch clinical trial on standardized formulation of mineral extracts.

• Journal of Biomedical Engineering Research
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• v.31 no.3
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• pp.234-239
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• 2010

We have developed and reported several micro-spikes for minimally invasive biopsy. This paper presents a diagnosis protocol for micro-spike biopsy using paraffin-based tissue collecting tool. Using the proposed tissue collecting tool, which has a negative micro-spike structure in a porous chamber, the biopsied tissue in a micro-spike is effectively detached. The proposed diagnosis protocol prevents the loss of tissues in a paraffin embedding and sectioning process. Hence, it is compatible with conventional histopathology without additional reagents and processes. The gastro-intestinal tissue of a pig is biopsied in an in vivo environment, and then it is detached from a micro-spike using the paraffin-based tissue collecting tool. A histopathological photomicrograph of the detached tissue is acquired with the proposed diagnosis protocol. The acquired image offers clinical quality. This result shows that the paraffin-based tissue collecting tool is applicable to the medical practice.

• Journal of Veterinary Clinics
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• v.22 no.4
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• pp.431-434
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• 2005

A 2-year-old castrated male, Cocker spaniel dog with a history of chronic productive cough for 2 to 3 months and with unsuccessful treatment was referred to Veterinary Medical Teaching Hospital, Seoul National University. On thoracic radiographs, there were alveolar infiltrations at left cranial and right caudal lung fields, and soft-tissue opacity round to oval images at overall lung field. The bronchi were dilated, tortuous and not tapered. Abnormal air was accumulated focally in the caudodorsal lung fields. To scrutinize the soft-tissue opacity image and accumulated air, computed tomography (CT) was done. On CT images, severe cylindrical or tubular bronchiectasis was confirmed. And the soft-tissue opacity images were found in the dilated bilated and thought to complexes of mucous plugs, inflammatory cells, necrotic and fibrotic tissue. The dog was dead next day to the CT scan, so necropsy and histopathologic examination were perfermed. On the histopathology, there were cylindrical bronhiectasis and severe diffuse chronic fibrinous necropurulent bronchitis and bronchopneumonia. In this case, it was difficult to diagnose the bronchiectasis only with radiography due to the concurrent lesions, such as pulmonary infiltrations and mucous plugs, which was identified by computed tomography. Thus, computed tomography is considered as a useful modality to confirm tile bronchiectasis camouflaged by the concurrent lesion.