CADBOSS: A computer-aided diagnosis system for whole-body bone scintigraphy scans

Aslantaş, Ali; Dandıl, Emre; Saǧlam, Semahat; Çakıroğlu, Murat

doi:10.4103/0973-1482.150422

Cited by 28 publications

(18 citation statements)

References 6 publications

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“…However, our work is devoted to the investigation of deep-learning algorithms on bone metastasis diagnosis in breast cancer patients using bone scintigraphy. In nuclear medicine, bone scintigraphy and PET/CT are the two most used methods for bone metastasis detection in breast cancer [61][62][63][64]. Concerning the involvement and applicability of CNNs in diagnosis of metastatic breast cancer in bones from whole-body scan images, no previous works in CNNs exploration are reported.…”

Section: Related Research In Breast Cancer Diagnosis Using Convolutiomentioning

confidence: 99%

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

et al. 2020

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(1) Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.

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Section: Related Research In Breast Cancer Diagnosis Using Convolutiomentioning

confidence: 99%

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

et al. 2020

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“…In 2016, Aslantas et al proposed “CADBOSS” as a fully automated diagnosis system for bone metastases detections, using whole-body images [ 56 ]. The proposed CAD system combines an active contour segmentation algorithm for hotspots detection, an advanced method of image gridding to extract certain characteristics of metastatic regions, as well as an ANN classifier for identifying possible metastases.…”

Section: Introductionmentioning

confidence: 99%

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application

et al. 2020

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Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machine learning with emphasis on modern efficient convolutional neural networks (CNNs) algorithms, for the diagnosis of prostate cancer metastasis from bone scintigraphy images. The advantageous and outstanding capabilities of deep learning, machine learning's groundbreaking technological advancement, have not yet been fully investigated regarding their application in computer-aided diagnosis systems in the field of medical image analysis, such as the problem of bone metastasis classification in whole-body scans. In particular, CNNs are gaining great attention due to their ability to recognize complex visual patterns, in the same way as human perception operates. Considering all these new enhancements in the field of deep learning, a set of simpler, faster and more accurate CNN architectures, designed for classification of metastatic prostate cancer in bones, is explored. This research study has a two-fold goal: to create and also demonstrate a set of simple but robust CNN models for automatic classification of whole-body scans in two categories, malignant (bone metastasis) or healthy, using solely the scans at the input level. Through a meticulous exploration of CNN hyper-parameter selection and fine-tuning, the best architecture is selected with respect to classification accuracy. Thus a CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients. The achieved classification testing accuracy is 97.38%, whereas the average sensitivity is approximately 95.8%. Finally, the best-performing CNN method is compared to other popular and well-known CNN architectures used for medical imaging, like VGG16, ResNet50, GoogleNet and MobileNet. The classification results show that the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.

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“…Some experiment done for selecting a suitable gamma value the result shows that an image enhanced with gamma value of 0.6 was the best and this result was the same of that shown by Zobly. Aslantas et al [15] used feature extraction and classification to detect the hotspot in the body. His result shows that the algorithm Identify 120 out of 130 images.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Different Image Enhancement Methods for Effective Whole-Body Bone Scan Image

Zobly¹

2019

ABB

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Nuclear medicine is one of the most important diagnostic tools used for different types of investigations such as a thyroid scan, renal function and whole-body bone scan. The main problem in nuclear medicine imaging system is the resulting images degraded with large amounts of noise. In this work we want use four different enhancement methods to enhance wholebody bone scan image so as to reduce the noise from the image and improve the resolution to achieve a better image quality and maintain quality for accurate diagnosis. Histogram equalization, adaptive histogram equalization, log transformation and gamma correction algorithms were used to improve the image quality. Four pair of bone scan images from gamma camera were used to perform this work. Enhanced images were quantified and evaluated by calculating the Peak Signal Noise Ratio, Mean Square Error and entropy. The result shows that the gamma correction algorithm gives best result among the four algorithms used for enhancing the bone images. The gamma correction algorithm can assist the radiologist in diagnosis the patient and quantify any changes accurately and quickly.

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CADBOSS: A computer-aided diagnosis system for whole-body bone scintigraphy scans

Abstract: Detailed experiments showed that CADBOSS outperforms state-of-the-art computer-aided diagnosis. (CAD) systems and reasonably improves physician' diagnostic success.

Cited by 28 publications

References 6 publications

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application

Comparison of Different Image Enhancement Methods for Effective Whole-Body Bone Scan Image

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