Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

Azizi, Shekoofeh; Woudenberg, Nathan Van; Sojoudi, Samira; Li, Ming; Xu, Sheng; Anas, Emran Mohammad Abu; Yan, Pingkun; Tahmasebi, Amir M.; Kwak, Jin Tae; Türkbey, Barış; Choyke, Peter L.; Wood, Bradford J.; Mousavi, Parvin; Abolmaesumi, Purang

doi:10.1007/s11548-018-1749-z

Cited by 9 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other studies investigated the role of radio-frequency time-series data 93,94 to detect prostate cancer using traditional machine learning. Moreover most of these studies focused on the task of lesion classification (classifying a physician-outlined region of interest into benign vs cancerous tissue) 72,90,91,93,94 while only a few focused on lesion detection (detecting and localizing cancer on the entire ultrasound image). 80,89,92 Summary.…”

Section: Cancer Detection On Prostate Ultrasound Imagesmentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

show abstract

Section: Cancer Detection On Prostate Ultrasound Imagesmentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

show abstract

“…In the field of prostate cancer detection and its grading, a group of researchers has conducted several studies [18][19][20][21][22][23][24]. They used multi-parametric magnetic resonance imaging (MRI) data of the prostate gland, CEUS imaging data of suspicious lesions in multiparametric MRI, and histopathologic results of MRI and transrectal US (TRUS)-fusion guided targeted biopsies.…”

Section: Challenges Applying Deep Learning To Abdominal Us Imagingmentioning

confidence: 99%

Current status of deep learning applications in abdominal ultrasonography

Song

2021

Ultrasonography

View full text Add to dashboard Cite

Ultrasonography. 2020 Sep 2. Epub ahead of print Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications.

show abstract

“…The same group later incorporated a DL back-end into a platform for real-time prostate biopsy. [66][67][68] An RNN with long short-term memory (LSTM) cells was used for prostate cancer classification while ResNets and dilated CNNs were used for prostate segmentation.…”

Section: Us Image Analysismentioning

confidence: 99%

“…Domain adaptation and transfer learning techniques were combined to train a tissue classification model on RF data (source domain) and B‐mode data (target domain). The same group later incorporated a DL back‐end into a platform for real‐time prostate biopsy 66–68 . An RNN with long short‐term memory (LSTM) cells was used for prostate cancer classification while ResNets and dilated CNNs were used for prostate segmentation.…”

Section: Ai Applications In Qusmentioning

confidence: 99%

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey

Zhou

Yang

Lan

2021

J of Ultrasound Medicine

View full text Add to dashboard Cite

Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter-and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS.Finally, challenges and future potential AI applications in QUS are discussed.

show abstract

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

Abstract: Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Cited by 9 publications

References 30 publications

A review of artificial intelligence in prostate cancer detection on imaging

A review of artificial intelligence in prostate cancer detection on imaging

Current status of deep learning applications in abdominal ultrasonography

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey

Contact Info

Product

Resources

About