Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

Rundo, Leonardo; Militello, Carmelo; Russo, Giorgio; Garufi, Antonio; Vitabile, Salvatore; Gilardi, Maria Carla; Mauri, Giancarlo

doi:10.3390/info8020049

Cited by 51 publications

(53 citation statements)

References 72 publications

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“…Classic methods mainly leveraged atlases [19,26] or statistical shape priors [27]: atlas-based approaches realized accurate segmentation when new prostate instances resemble the atlas, relying on a non-rigid registration algorithm [27,28]. Unsupervised clustering techniques allowed for segmentation without manual labeling of large-scale MRI datasets [17,29].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, according to the Prostate Imaging-Reporting and Data System version 2 (PI-RADS TM v2) [16], radiologists must perform a zonal partitioning before assessing the suspicion of PCa on multi-parametric MRI. However, an improved PCa diagnosis requires a reliable and automatic zonal segmentation method, since manual delineation is time-consuming and operator-dependent [17,18]. Moreover, in clinical practice, the generalization ability among multi-institutional prostate MRI datasets is essential due to large anatomical inter-subject variability and the lack of a standardized pixel intensity representation for MRI (such as for CT-based radiodensity measurements expressed in Hounsfield units) [19].…”

Section: Introductionmentioning

confidence: 99%

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USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

et al. 2019

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Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i ) training on either each individual dataset or multiple prostate MRI datasets and (ii ) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three stateof-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/crossdataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

et al. 2019

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“…Rundo et al [8] proposed Fuzzy C-Means (FCM) clustering algorithm for prostate multispectral MRI morphologic data processing and segmentation. The authors used co-registered T1w and T2w MR image series and achieved an average dice similarity coefficient 90.77 ± 7.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

et al. 2019

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An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature. Appl. Sci. 2019, 9, 2969 2 of 17 practitioners, for both diagnosis and treatment. Usually, the cancer detection process in histopathology consists of categorizing stained microscopic biopsy images into malignant and benign.The Gleason grade grouping system defines Gleason scores ≤ 6 as grade 1, score 3 + 4 = 7 as grade 2, score 4 + 3 = 7 as grade 3, score 4 + 4, 3 + 5 or 5 + 3 = 8 as grade 4, and score 4 + 5, 5 + 4 or 5 + 5 = 9 or 10 as grade 5. The Gleason score is obtained by adding the primary (most common) and secondary (second most common) scores from H&E stained tissue microscopic images. This system was developed by Dr. Donald F Gleason, who was a Pathologist in Minnesota, and members of the Veterans Administration Cooperative Urological Research Group (VACURG) [3]. This system was tested on a large number of patients, including long-term follow-ups and is considered an outstanding success.In recent years, an excellent and important addition to microscopy and digital imaging has been developed for microscopes that are used to convert stained tissue slides into whole slide digital images. This allows for more efficient computer-based viewing and analysis of histopathology. Early diagnosis and treatment are required, to avoid the enlargement of cancer cells in the prostate gland and control the spreading of more aggressive tumors to other parts of the body.The digital pathology field has grown dramatically over recent years, largely due to technological advancements in image processing and machine learning algorithms, and increases in computational power. As part of this field, many methods have been proposed for automatic histopathological image analysis and classification. In this paper, color segmentation, based...

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“…The manual delineation of both prostate Whole Gland (WG) and PCa on MR images is a time-consuming and operator-dependent task, which relies on experienced physicians [5]. Besides WG segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) of the prostate can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions [6,7]; the PZ harbors 70 − 80% of PCa and is a target for prostate biopsy [8].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

Rundo

Han

Zhang

et al. 2019

Neural Approaches to Dynamics of Signal Exchanges

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Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of Convolutional Neural Networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.

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Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

Cited by 51 publications

References 72 publications

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

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