Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN)

Liu, Chang; Gardner, Stephen; Wen, Ning; Elshaikh, Mohamed A.; Siddiqui, Farzan; Movsas, Benjamin; Chetty, Indrin J.

doi:10.1016/j.ijrobp.2019.03.017

Cited by 77 publications

(86 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17 The HD was 7.0 AE 3.5 mm, 7.3 AE 2.0 mm, and 6.3 AE 2.0 mm for three groups. 17 Our results showed that the mean DSC and HD of 44 3D TRUS images were 0.92 AE 0.09 and 4.38 AE 4.66 mm, respectively. Although the mean metrics of our method is better than their results, the standard deviation of our method is larger than what Liu et al reported.…”

Section: Discussionmentioning

confidence: 81%

“…Supervised machine learning has demonstrated enormous potential in medical image segmentation. According to the training model used, these methods can be grouped into support vector machines (SVM)‐based, random forest (RF)‐based, and deep learning‐based methods . The SVM‐based and RF‐based methods use handcraft information, such as texture features, to train a SVM or RF classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Deep features, which are directly assigned with semantic and structural information of the prostate region, are used to train a deep learning‐based classifier. Liu et al used deep neural networks (DNN) to learn the deep features for prostate segmentation . DNN can automatically learn the deep features adaptive to the data, which differs from the handcrafted features.…”

Section: Introductionmentioning

confidence: 99%

“…According to the training model used, these methods can be grouped into support vector machines (SVM)-based, 15 random forest (RF)based, 16 and deep learning-based methods. 17,18 The SVMbased and RF-based methods use handcraft information, such as texture features, to train a SVM or RF classifier. However, handcraft features, such as histogram of oriented gradient (HoG), local binary pattern (LBP), and Haar wavelet features in SVM-and Haar-like features in RF-based methods, may not accurately represent the CT image.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

View full text Add to dashboard Cite

Purpose Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI‐CT registration errors. We developed a deep attention‐based segmentation strategy on CT‐based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. Methods and materials We developed a prostate segmentation strategy which employs an sMRI‐aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave‐one‐out experiments and validated with an additional 50 patients by hold‐out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI‐defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave‐one‐out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold‐out test. Conclusions We have proposed a novel CT‐only prostate segmentation strategy using CT‐based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.

show abstract

Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Intensive studies have been made regarding prostate CT automatic segmentation. Recently, the reported highest DSC is 0.88 ± 0.03 by Liu et al [12]using U-Net and 1114 ture CT cases. Our average result is 0.73±0.09 which is compatibe with Burgos et al [13] using multi-atlas based SynCT (0.73 DSC).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 94%

Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans

Liu

Khosravan

Stember

et al. 2019

Domain Adaptation and Representation Transfer and Medical Image Learning With Less Labels and Imperfect Data

View full text Add to dashboard Cite

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from Cycle-GAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.

show abstract

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application

Wen,

Chen,

Wang

et al. 2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

BackgroundRadiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer.MethodsComputed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto‐delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds.ResultsIn the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001).ConclusionUsing deep learning and attention mechanism, a highly consistent and time‐saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.

show abstract

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN)

Cited by 77 publications

References 20 publications

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application

Contact Info

Product

Resources

About