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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

27.03.2017 | Original Article

Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection

verfasst von: Shekoofeh Azizi, Parvin Mousavi, Pingkun Yan, Amir Tahmasebi, Jin Tae Kwak, Sheng Xu, Baris Turkbey, Peter Choyke, Peter Pinto, Bradford Wood, Purang Abolmaesumi

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2017

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Abstract

Purpose

We present a method for prostate cancer (PCa) detection using temporal enhanced ultrasound (TeUS) data obtained either from radiofrequency (RF) ultrasound signals or B-mode images.

Methods

For the first time, we demonstrate that by applying domain adaptation and transfer learning methods, a tissue classification model trained on TeUS RF data (source domain) can be deployed for classification using TeUS B-mode data alone (target domain), where both data are obtained on the same ultrasound scanner. This is a critical step for clinical translation of tissue classification techniques that primarily rely on accessing RF data, since this imaging modality is not readily available on all commercial scanners in clinics. Proof of concept is provided for in vivo characterization of PCa using TeUS B-mode data, where different nonlinear processing filters in the pipeline of the RF to B-mode conversion result in a distribution shift between the two domains.

Results

Our in vivo study includes data obtained in MRI-guided targeted procedure for prostate biopsy. We achieve comparable area under the curve using TeUS RF and B-mode data for medium to large cancer tumor sizes in biopsy cores (>4 mm).

Conclusion

Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.

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Titel: Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection
verfasst von: Shekoofeh Azizi
Parvin Mousavi
Pingkun Yan
Amir Tahmasebi
Jin Tae Kwak
Sheng Xu
Baris Turkbey
Peter Choyke
Peter Pinto
Bradford Wood
Purang Abolmaesumi
Publikationsdatum: 27.03.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1573-x

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Abstract

Purpose

Methods

Results

Conclusion

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