Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks

Purpose: Accurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. Methods and Materials: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate. Results: The mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013. Conclusions: Our research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI

Rodrigues, N. M., Silva, S., Vanneschi, L., & Papanikolaou, N. (2023). A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI. Cancers, 15(5), 1-21. [1467]. https://doi.org/10.3390/cancers15051467 --- Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952159 (ProCAncer-I). This work was partially supported by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020), and under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Nuno Rodrigues was supported by PhD Grant 2021/05322/BD.Prostate cancer is one of the most common forms of cancer globally, affecting roughly one in every eight men according to the American Cancer Society. Although the survival rate for prostate cancer is significantly high given the very high incidence rate, there is an urgent need to improve and develop new clinical aid systems to help detect and treat prostate cancer in a timely manner. In this retrospective study, our contributions are twofold: First, we perform a comparative unified study of different commonly used segmentation models for prostate gland and zone (peripheral and transition) segmentation. Second, we present and evaluate an additional research question regarding the effectiveness of using an object detector as a pre-processing step to aid in the segmentation process. We perform a thorough evaluation of the deep learning models on two public datasets, where one is used for cross-validation and the other as an external test set. Overall, the results reveal that the choice of model is relatively inconsequential, as the majority produce non-significantly different scores, apart from nnU-Net which consistently outperforms others, and that the models trained on data cropped by the object detector often generalize better, despite performing worse during cross-validation.publishersversionpublishe

Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research

Mixed-reality visualization environments to facilitate ultrasound-guided vascular access

Ultrasound-guided needle insertions at the site of the internal jugular vein (IJV) are routinely performed to access the central venous system. Ultrasound-guided insertions maintain high rates of carotid artery puncture, as clinicians rely on 2D information to perform a 3D procedure. The limitations of 2D ultrasound-guidance motivated the research question: “Do 3D ultrasound-based environments improve IJV needle insertion accuracy”. We addressed this by developing advanced surgical navigation systems based on tracked surgical tools and ultrasound with various visualizations. The point-to-line ultrasound calibration enables the use of tracked ultrasound. We automated the fiducial localization required for this calibration method such that fiducials can be automatically localized within 0.25 mm of the manual equivalent. The point-to-line calibration obtained with both manual and automatic localizations produced average normalized distance errors less than 1.5 mm from point targets. Another calibration method was developed that registers an optical tracking system and the VIVE Pro head-mounted display (HMD) tracking system with sub-millimetre and sub-degree accuracy compared to ground truth values. This co-calibration enabled the development of an HMD needle navigation system, in which the calibrated ultrasound image and tracked models of the needle, needle trajectory, and probe were visualized in the HMD. In a phantom experiment, 31 clinicians had a 96 % success rate using the HMD system compared to 70 % for the ultrasound-only approach (p= 0.018). We developed a machine-learning-based vascular reconstruction pipeline that automatically returns accurate 3D reconstructions of the carotid artery and IJV given sequential tracked ultrasound images. This reconstruction pipeline was used to develop a surgical navigation system, where tracked models of the needle, needle trajectory, and the 3D z-buffered vasculature from a phantom were visualized in a common coordinate system on a screen. This system improved the insertion accuracy and resulted in 100 % success rates compared to 70 % under ultrasound-guidance (p=0.041) across 20 clinicians during the phantom experiment. Overall, accurate calibrations and machine learning algorithms enable the development of advanced 3D ultrasound systems for needle navigation, both in an immersive first-person perspective and on a screen, illustrating that 3D US environments outperformed 2D ultrasound-guidance used clinically