Ciencias Exactas y Ciencias de la Salud

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Automatic detection and segmentation of prostate cancer using deep learning techniques
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-20) Quihui Rubio, Pablo César; González Mendoza, Miguel; puemcuervo, emimayorquin; Alfaro Ponce, Mariel; Mata Miquel, Christian; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, Gilberto
Prostate cancer is a major cause of death among men worldwide, and detecting it usually involves invasive procedures. Magnetic resonance imaging (MRI) has become a common research area for detecting this cancer because it represents a less invasive option. However, segmenting the prostate gland from MRI images can be a complicated task that requires expert opinions, which is both time-consuming and inconsistent. This thesis proposes a novel deep-learning architecture to automate and obtain accurate and reliable segmentation of the prostate gland in MRI scans. Precise segmentation is crucial for radiotherapy planning, as it determines the tumor’s location and size, which affects treat- ment effectiveness and reduces radiation exposure to surrounding healthy tissues. Therefore, a thorough comparison between architectures from the state-of-the-art is also performed. Convolutional neural networks have shown great potential in medical image segmenta- tion, but the uncertainty associated with their predictions is often overlooked. Therefore, this work proposes a novel approach incorporating uncertainty quantification to ensure reliable and trustworthy results. The models were evaluated on a dataset of prostate T2-MRI scans obtained in collab- oration with the Centre Hospitalarie Dijon and Universitat Politecnica de Catalunya. The results showed that the proposed architecture FAU-Net outperforms most existing models in the literature, with an improvement of 5% in the Dice Similarity Coefficient (DSC) and In- tersection over Union (IoU). However, the best model overall was R2U-Net, which achieved segmentation accuracy and uncertainty estimation values of 85% and 76% for DSC and IoU, respectively, with an uncertainty score lower than 0.05. In addition to the proposed model and comparison between models for prostate seg- mentation and uncertainty quantification, a web application was presented for easier access to the trained models in a clinical setting. This web app would allow medical professionals to upload MRI scans of prostate cancer patients and obtain accurate and reliable segmentation quickly and easily. This would reduce the time and effort required for manual segmentation and improve patient outcomes by facilitating better treatment planning. Overall, this work presents a novel strategy for prostate segmentation using deep learn- ing models and uncertainty quantification. The proposed method provides a reliable and trust- worthy segmentation while quantifying the uncertainty associated with the predictions. This research can benefit prostate cancer patients by improving treatment planning and outcomes.
Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for cerebral angiography
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11) Herrera Montes de Oca, Daniela; González Mendoza, Miguel; puemcuervo, emipsanchez; Alfaro Ponce, Mariel; Mata Miquel, Christian; Falcon Morales, Luis Eduardo; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, Gilberto
The anatomical variations of the cerebrovascular system of infants may have an impact on the neurological development of the infants and may even lead to death. Intracranial vascular abnormalities include multiple diseases such as arteriovenous malformations and moyamoya. For evaluating this kind of diseases, there are non-invasive imaging techniques such as TRANCE which is a type of MRI. It allows the visualization of the vasculature isolated from the white matter. In the process of analyzing the images, they go through manual techniques for processing the images and segmenting the vessels depending on the patient. Besides the images are challenging to analyze due to the nature of the images they present noise related to their acquisition. This is difficult not only for the analysis and the diagnosis of the specialists but also for the performance of AI techniques. This thesis focuses on the problem of denoising these images and evaluating them using unsupervised methods due to the lack of clean images available and in the automation of segmentation. The purpose is to enhance the images for a better visual analysis of the specialist and segmentation for improving the quantification by doing feature extraction of the vessels. For doing so a pipeline was proposed. It consists of using a combination of traditional methods and deep learning-based unsupervised methods for denoising the images. The results were evaluated quantitatively using non-reference image quality evaluators and qualitatively by specialists. For the segmentation, a model was trained using noisy images. Then it was tested using the noisy images and the denoised ones. In total there were 6 comparisons of denoising techniques. The use of unsupervised denoising models utilizing noise2void and probabilistic Noise2Void, in contrast to the application of traditional approaches, as well as the combination of both was compared. The non-reference image quality evaluators, the NIQE and PIQE scores, were used to evaluate the results qualitatively and quantitatively. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction of noise in the central and most dense areas, according to the qualitative results. The segmentation was done with a UNet model. The two approaches were compared. The results showed that training the model with noisy images and obtaining the segmentation using the denoised images showed an improvement of 9.4\% of the dice score and almost 16\% in the Hausdorff distance.

Ciencias Exactas y Ciencias de la Salud

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