Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting

doi:10.1016/j.media.2017.08.006

Cited by 130 publications

(122 citation statements)

References 32 publications

Supporting

Mentioning

114

Contrasting

Order By: Relevance

“…Although the reported results were accurate (86% detection rate at 20% false‐positive rate), the studied dataset was very small. Yang et al introduced a machine‐learning‐based method using mp‐MRI including T 2 W and ADC map sequences to, first, automatically determine whether each slice of the mp‐MRI contains PCa or not, and second, for slices classified as positive, to localize the position of the PCa for further targeted prostate biopsies . As a first step, T 2 W and ADC map MR images were registered.…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al introduced a machine-learning-based method using mp-MRI including T 2 W and ADC map sequences to, first, automatically determine whether each slice of the mp-MRI contains PCa or not, and second, for slices classified as positive, to localize the position of the PCa for further targeted prostate biopsies. 32 As a first step, T 2 W and ADC map MR images were registered. The position of the prostate whole gland was then found using a network trained on T 2 W MR images.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…This study was approved by our local institutional review board. The mp-MRI data used in the study were collected from two datasets: 1) A locally collected dataset named TJPCa Dataset [7], [8], [6] includes data conforming to the following five criteria: 1) the data for PCa assessment were acquired between June 2014 and December 2015; 2) all data included either pathologically-proven PCa or benign prostatic hyperplasia (BPH) by a 12-core systematic TRUS guided plus targeted prostate biopsy which were performed within six weeks after the MRI examination; 3) the data were from the patients who did not receive focal therapy, hormones, or radiation prior the MRI scan; 4) the data include both ADC and T2w images; and 5) the imaging data do not include severe artifacts that made the examination nondiagnostic. [30], [4], [31], includes data of 70 MRItargeted biopsy-proven CS PCa and 134 nonCS PCa patients.…”

Section: A Data Collectionmentioning

confidence: 99%

“…iv) We further evaluated the TrainSet with respect to these metrics which are presented in the 5 th row. For fairness, we augmented real CS PCa data to 1942 using the data augmentation approach proposed in [8] for training the multimodal classifier. By comparing all rows, we observe that the Real Data achieves the highest IS values and the lowest FID value among all synthesis methods, implying that there still exists room for improvement in synthesizing truly realistic and varied mp-MRI data.…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

See 1 more Smart Citation

Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization

Wang

Lin

Cheng

2020

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients (ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate cancer (PCa) via semi-supervised adversarial learning. Specifically, our synthesizer generates mp-MRI data in a sequential manner: first generating ADC maps from 128-d latent vectors, followed by translating them to the T2w images. The synthesizer is trained in a semisupervised manner. In the supervised training process, a limited amount of paired ADC-T2w images and the corresponding ADC encodings are provided and the synthesizer learns the paired relationship by explicitly minimizing the reconstruction losses between synthetic and real images. To avoid overfitting limited ADC encodings, an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images. To improve the robustness of synthesizing, we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only. A StitchLayer is then employed to fuse sub-images together in an interlaced manner into a full-size image. To enforce the synthetic images to indeed contain distinguishable CS PCa lesions, we propose to also maximize an auxiliary distance of Jensen-Shannon divergence (JSD) between CS and nonCS images. Experimental results show that our method can effectively synthesize a large variety of mp-MRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship. Compared to the state-of-the-art synthesis methods, our method achieves a significant improvement in terms of both visual and quantitative evaluation metrics.

show abstract

Artificial Intelligence in Diagnostic Imaging

Sollini

Loiacono

Volpe

et al. 2023

Radiology‐Nuclear Medicine Diagnostic Imaging

View full text Add to dashboard Cite

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI

Cited by 130 publications

References 32 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization

Artificial Intelligence in Diagnostic Imaging

Contact Info

Product

Resources

About