JULIANA HIRAOKA CATANI
Índice h a partir de 2011
2
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Radiologia, Hospital das Clínicas, Faculdade de Medicina - Médico
8 resultados
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- Radiological findings of breast involvement in benign and malignant systemic diseases(2018) MATSUMOTO, Renato Augusto Eidy Kiota; CATANI, Juliana Hiraoka; CAMPOY, Mirela Liberato; OLIVEIRA, Arthur Magalhães; BARROS, Nestor deAbstract Although the primary purpose of periodic mammograms in screening programs is to identify lesions suspected of being carcinomas, the findings are often related to systemic (benign or malignant) diseases, rather than breast cancer. Although the involvement of breast structures in systemic diseases is unusual, it can be included in the differential diagnosis of masses, skin changes, calcifications, asymmetry, and axillary lymphadenopathy. The main diagnostic entities that can be associated with such involvement are diabetes, chronic kidney disease, heart diseases, connective tissue diseases, HIV infection, lymphoma, leukemia, and metastases from primary tumors at other sites. In many cases, information related to knowledge and treatment of chronic diseases is not available to the radiologist at the time of evaluation of the mammography findings. The purpose of this essay is to offer relevant pictorial information to the general radiologist about systemic diseases involving the breast, expanding the range of differential diagnoses in order to avoid unnecessary invasive procedures.
bookPart Mamografia(2022) CATANI, Juliana Hiraoka- Exploratory Learning with Convolutional Autoencoder for Discrimination of Architectural Distortion in Digital Mammography(2019) OLIVEIRA, Helder C. R. de; MELO, Carlos F. E.; CATANI, Juliana H.; BARROS, Nestor de; VIEIRA, Marcelo A. da CostaThis work presents a deep learning approach based on autoencoder to improve the detection of architectural distortion (AD) in digital mammography. AD can be the earliest sign of breast cancer, appearing before the formation of any mass or calcification. However, it is very difficult to be detected and almost 50% of the cases are missed by the radiologists. Thus, we designed an autoencoder, based on a convolutional neural network (CNN), to work as a feature descriptor in a computer-aided detection (CAD) pipeline with the objective of detecting AD in digital mammography. This model was trained with 140,000 regions-of-interest (ROI) extracted from clinical mammograms. These samples were divided in two groups, with and without AD, according to the radiologist's report. Validation was done comparing the classifier performance when using the proposed autoencoder and other well-known feature descriptors, commonly used for the task of detecting AD in digital mammograms. The results showed that the performance of the autoencoder is slightly higher than that of other descriptors. However, the complexity and the computational cost of the autoencoder is much higher when compared to the hand-crafted descriptors.
- Detection of Architectural Distortion with Deep Convolutional Neural Network and Data Augmentation of Limited Dataset(2019) COSTA, Arthur C.; OLIVEIRA, Helder C. R.; CATANI, Juliana H.; BARROS, Nestor de; MELO, Carlos F. E.; VIEIRA, Marcelo A. C.Early detection of breast cancer can increase treatment efficiency. One of the earliest signs of breast cancer is the Architectural Distortion (AD), which is a subtle contraction of the breast tissue, most of the time unnoticeable. A lot of techniques have been proposed over the years to aid the detection of AD in digital mammography but only a few using a deep learning approach. One of the most successful algorithms of deep neural architecture are the Convolutional Neural Networks (CNNs). However, to assure better CNN performance, the training step requires a large volume of data. This paper presents a deep CNN architecture designed for the automatic detection of AD in digital mammography images. For the training step, we considered the data augmentation approach, to overcome the limitation of clinical dataset. CNN performance was evaluated in terms of Receiver Operating Characteristic (ROC). The measured area under the ROC curve (AUC) was 0:87 for the proposed CNN in the task of AD detection in digital mammography.
- Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography(2018) OLIVEIRA, Helder C. R. de; MENCATTINI, Arianna; CASTI, Paola; MARTINELLI, Eugenio; NATALE, Corrado di; CATANI, Juliana H.; BARROS, Nestor de; MELO, Carlos F. E.; GONZAGA, Adilson; VIEIRA, Marcelo A. C.This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.
bookPart Ultrassonografia modo B, color Doppler e elastografia(2022) CATANI, Juliana Hiraoka; ENDO, Érica; CASTRO, Flávio Spinola- A New Texture Descriptor Based on Local Micro-Pattern for Detection of Architectural Distortion in Mammographic Images(2017) OLIVEIRA, Helder C. R. de; MORAES, Diego R.; RECHE, Gustavo A.; BORGES, Lucas R.; CATANI, Juliana H.; BARROS, Nestor de; MELO, Carlos F. E.; GONZAGA, Adilson; VIEIRA, Marcelo A. C.This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).
- A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices(2019) OLIVEIRA, Helder C. R. de; MENCATTINI, Arianna; CASTI, Paola; CATANI, Juliana H.; BARROS, Nestor de; GONZAGA, Adilson; MARTINELLI, Eugenio; VIEIRA, Marcelo A. da CostaBackground and objective: Full-field digital mammography (FFDM) is the predominant breast cancer screening exam used. However, with the emergence of digital breast tomosynthesis (DBT) the radiologists could improve early recognition of breast cancer signs. In this scenario, the detection of architectural distortion (AD) is still a challenging task. ADs are very subtle contraction of the breast parenchyma that could represent the earliest manifestation of cancer, assessing at present 50% of missed cases. Methods: This paper proposes a new paradigm to detect AD in DBT exams by a cross-cutting approach exploiting the 3-dimensionality of the imaging modality. After locating AD candidates in each DBT slice, the suspicious spots are tracked in cross-slice direction and then characterized in terms of neighboring texture. In this approach, which mimics radiologist's scrolling down over zoomed slices, we reduce the amount of uninformative signs collected in DBT exams by preserving the large variability of AD appearance. Results: Using 37 sets of DBT slices containing at least one AD locus indicated by a radiologist, the proposed methodology reaches an AUC of 0.84, with only one false negative exam at sensitivity of 0.9. Conclusions: The results show that the proposed algorithm can be a promising tool for the automatic detection of AD locii. Future work will address the extension of the dataset of DBT slices as well the improvement of algorithm performance toward the application in the clinical practice. (C) 2019 Published by Elsevier Ltd.