Segmentação de vasos sanguíneos utilizando redes neurais convolucionais: investigação da prevalência de descontinuidades e desenvolvimento de técnicas para mitigá-las
Resumo
With the current advancements in technology and medical techniques, the pursuit of
improving diagnostic methods and scientific analyses brings forth a significant challenge:
the efficiency and effectiveness in processing clinical data. In the field of medical image
processing, there is a crucial phase that can influence all subsequent steps and even the
final diagnosis, which is the segmentation phase. Particularly, segmentation is vital in
examinations involving images of blood vessels, as these structures pose a great difficulty
in analysis due to their complex and thin nature. Seeking improvements in segmentation
techniques is of paramount importance, considering it is a highly sensitive phase of analysis,
and a simple change of lens or imaging acquisition device can compromise the quality of
the samples. Literature studies focus on quantifying the quality of segmentation methods
using global metrics such as accuracy, precision, and recall, but often this focus may lead to
problems in vessel geometry, where important information such as bifurcations, continuity,
and diameter are lost, thereby causing various diagnostic issues. This work focuses on
the analysis of continuity problems in vessel segmentation, i.e., cases in which parts of
vessels are not correctly detected. An accuracy metric is defined to specifically quantify the
segmentation quality in regions of vessels that are difficult to segment. It is demonstrated
that this metric enables more precise quantification of the quality of low-saliency vessel
detection in images than traditional metrics. Additionally, a data augmentation technique
is defined for training neural networks, enabling improved segmentation quality of low-
saliency vessels. The technique involves the creation of regions with drops in intensity and
vessel discontinuities. Based on the analyses conducted, it is expected that the developed
techniques can assist in improving diagnoses and future research in biology, creating new
possibilities for addressing segmentation problems.
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