Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images

Fourcade, Constance; Ferrer, Ludovic; Santini, Gianmarco; Moreau, Noémie; Rousseau, Caroline; Lacombe, M.; Guillerminet, Camille; Colombié, Mathilde; Campone, Mario; Mateus, Diana; Rubeaux, Mathieu

doi:10.1109/embc44109.2020.9175683

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…They reported a per-tumor sensitivity of 90.9%. Fourcade et al [ 77 ] used a combination of DL and superpixel segmentation-based methods to segment full body organs such as the brain and heart from Positron Emission Tomography (PET) images. To synthetically increase the size of the dataset, the authors deployed rotations, scaling, mirroring, and elastic deformations.…”

Section: Current Applications Of Deep Learning In Cancer Diagnosis Prognosis and Predictionmentioning

confidence: 99%

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail

Kaabar

et al. 2021

Computational and Mathematical Methods in Medicine

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Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

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Section: Current Applications Of Deep Learning In Cancer Diagnosis Prognosis and Predictionmentioning

confidence: 99%

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail

Kaabar

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Combining light scattering tomography with other imaging methods such as ultrasound imaging, molybdenum target imaging, and nuclear magnetic resonance imaging (MRI) can overcome these shortcomings and has good application prospects [6]. Ultrasound-guided positioning light scattering tomography can measure the difference in light absorption of breast lesions and surrounding normal tissues through two wavelengths in the near-infrared band and finally detect the relevant indicators of the diseased tissues because the level of hemoglobin concentration can quantitatively map the amount of neovascularization in the tumor and achieve the purpose of distinguishing breast lesions from benign and malignant tumors [7]. In the era of big data, the use of current medical data, combined with artificial intelligence learning methods, can give full play to the powerful thrust of technology on medical progress [8].…”

Section: Introductionmentioning

confidence: 99%

Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms

Xiao

Gan

2022

Computational Intelligence and Neuroscience

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This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patients with breast tumor were included in this study, all of whom underwent routine ultrasound examinations. Subsequently, TUI was used to assist in guiding the positioning, and the light scattering tomography system was used to further measure the lesions. A deep learning model was established to process the imaging results, and the pathological test results were undertaken as the gold standard for the efficiency of different imaging methods to diagnose the breast tumors. The results showed that, among 120 patients with breast tumor, 56 were benign lesions and 64 were malignant lesions. The average total amount of hemoglobin (HBT) of malignant lesions was significantly higher than that of benign lesions (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of TUI in the diagnosis of breast cancer were 90.4%, 75.6%, 81.4%, 84.7%, and 80.6%, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of ultrasound in the diagnosis of breast cancer were 81.7%, 64.9%, 70.5%, 75.9%, and 80.6%, respectively. In addition, for suspected breast malignant lesions, the combined application of ultrasound and tomography can increase the diagnostic specificity to 82.1% and the accuracy to 83.8%. Based on the above results, it was concluded that TUI combined with ultrasound had a significant effect on benign and malignant diagnosis of breast cancer and can significantly improve the specificity and accuracy of diagnosis. It also reflected that deep learning technology had a good auxiliary role in the examination of diseases and was worth the promotion of clinical application.

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Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets

2021

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Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images

Cited by 7 publications

References 13 publications

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms

Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets

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