A CUDA-powered method for the feature extraction and unsupervised analysis of medical images

Funder: Università degli Studi di Milano - BicoccaAbstractImage texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick &amp; SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to $$\sim 19.5\times$$ ∼ 19.5 × and $$\sim 37\times$$ ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research.</jats:p

A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation

Purpose: Magnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. Method: To address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments. Results: The proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 [pixels], MAXD=6.233 [pixels] and HD=2.988 [pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches. Conclusions: The proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution

A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation

Purpose: Magnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. Method: To address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments. Results: The proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 [pixels], MAXD=6.233 [pixels] and HD=2.988 [pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches. Conclusions: The proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution