A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Shahedi, Maysam; Halicek, Martin; Li, Qinmei; Liu, Lizhi; Zhang, Zhenfeng; Verma, Sadhna; Schuster, David M.; Fei, Baowei

doi:10.1117/12.2512282

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…In our experiments, 14 images were segmented by a second expert, and the inter-expert agreement was found to be of 0.8794 in terms of Sørensen-Dice Similarity Coefficient (DSC) and 1.5619 mm in terms of the Average Boundary Distance (ABD). Very similar results were obtained in [3], with experts achieving a DSC and an ABD of 0.83 and 1.5 mm, respectively. Because of this, automatic segmentation algorithms for the prostate are increasingly sought-after.…”

Section: Introductionsupporting

confidence: 77%

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Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

et al. 2021

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Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.

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Section: Introductionsupporting

confidence: 77%

“…Despite multiple attempts at incorporating a similar loss to our model, we finally decided against it, since it did not provide any performance advantages during validation. Therefore, the finally used loss function L is directly derived from DSC, as illustrated in Equation (3).…”

Section: Evaluation Metrics and Lossmentioning

confidence: 99%

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

et al. 2021

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“…These results suggest that the use of 15-20 sparse manually selected surface points achieves a segmentation performance close to manual segmentation. According to our previous studies 13,14 , manual selection of 12 prostate surface points on both MRI and CT images could be done within 20 seconds, which is considerably shorter than the average time of manual prostate MRI segmentation, as reported in the literature 15,16 . Therefore, we think minimal user interaction could be helpful to improve the segmentation accuracy significantly.…”

Section: Testing Resultsmentioning

confidence: 85%

“…Therefore, an observer study is required to confirm the effectiveness of this approach for prostate segmentation in a real clinical situation. In addition, although our previous studies 13,14,17 showed that using minimal user interaction for prostate segmentation could substantially speed up the process when compared to manual segmentation time, the observer study must confirm that for this study.…”

Section: Limitationsmentioning

confidence: 72%

Incorporating minimal user input into deep learning based image segmentation

Shahedi

Halicek

Dormer

et al. 2020

Medical Imaging 2020: Image Processing

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Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dualinput path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.

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“…These datasets were resampled to the same target spacing (2, 2, 2) and embedded into a 256 × 256 × 256 3D volumetric space [35]. After normalizing and window leveling [−200, 250] [36][37][38][39], to enhance the contrast and texture of soft tissue, the foreground of input voxels was selected from the background by an intersection with mask voxels images using MATLAB R2022a. To increase the amount of data for training the network, we augmented the CT images…”

Section: Patient Cohorts and Data Pre-processingmentioning

confidence: 99%

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

Cho,

Lee,

Kim

et al. 2023

Cancers

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U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net (“no-new-Net”). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.

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A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Cited by 4 publications

References 32 publications

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

Incorporating minimal user input into deep learning based image segmentation

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

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