Mr. Rodrigo Ramos Profile

Rodrigo Ramos

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Proceedings Article | 18 June 2024 Presentation + Paper

Minimal FCN for image segmentation

Rodrigo Ramos, Jimena Olveres, Boris Escalante-Ramírez

Proceedings Volume 12998, 1299808 (2024) https://doi.org/10.1117/12.3022259

KEYWORDS: Image segmentation, Education and training, Data modeling, Semantics, Image classification, Lung, Classification systems, Statistical modeling, Medical imaging, Batch normalization

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Research on interpretable CNN classifiers, involves comparing semantic segmentation masks with heat maps designed as visual explanations. A robust explanation accurately identifies or approximates the segmentation of an object. Our focus is on CNN classifiers with enhanced explainability, particularly in the middle layers. To explore this, we propose testing an encoder, trimmed to a medium layer, within a Fully Convolutional Network (FCN). Semantic segmentation is a pivotal task in computer vision preceding object recognition, and demands efficiency to optimize performance, energy consumption, and hardware costs. While various lightweight FCN proposals exist for distinct semantic segmentation tasks, their designs often introduce additional complexity compared to the more basic FCN design we advocate. Our goal is to see how well a minimal FCN works in a simple semantic segmentation task using medical images and how its accuracy changes when the training dataset is shrunk. The study involves characterizing and comparing our minimal FCN against other lightweight deep segmentation models and analyzing accuracy curves concerning the quantity of training data. Utilizing chest CT imaging, we focus on segmenting the lungs. We highlight the importance of data consumption and model size as decisive factors in selecting an architecture, especially when differences in predictive accuracy are marginal. Characterizing deep architectures based on their data requirements, allows for a thorough comparison fostering a deeper understanding of their suitability for specific applications.

Proceedings Article | 1 April 2020 Presentation + Paper

Deep learning approach for cerebellum localization in prenatal ultrasound images

Rodrigo Ramos, Jimena Olveres, Boris Escalante-Ramírez, Fernando Arámbula Cosío

Proceedings Volume 11353, 1135322 (2020) https://doi.org/10.1117/12.2556818

KEYWORDS: Cerebellum, Ultrasonography, Image segmentation, Fetus, Medical imaging, Brain, Binary data, Image processing, Neural networks, Feature extraction

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Ultrasound (US) has become one of the most common forms for medical imaging in clinical practice. It is a non-invasive and safe practice that allows obtaining images in real time. It is also a technology with important challenges such as low image quality and high variability (between manufacturers and institutions) [1]. This work aims to apply a fast and accurate deep learning architecture to detect and locate cerebellum in prenatal ultrasound images. Cerebellum biometry is used to estimate fetal age [2] and cerebellum segmentation could be applied to detect malformation [3]. YOLO (You Only Look Once) is a convolutional neural network (CNN) architecture for detection, classification and location of objects in images [4]. YOLO was innovative because it solved a regression problem to predict the location (coordinates and sizes) of bounding boxes and associated classes. We used 316 ultrasound scans of fetal brains and their respective cerebellar segmentations. From these, 78 images were randomly taken to be treated as test images and the rest were available to feed the trainings. Segmentation masks were converted to numerical descriptions of bounding boxes. To deal with small data set, transfer learning was done by initializing convolutional layers with weights pretrained on Imagenet [5]. We evaluated detection using F1 score and localization using average precision (AP) for 78 test images. Our best AP was 84.8% using 121 divisions or cells per image. Future work will focus on segmentation task assisted by localization.

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