The image shows the predicted boundary boxes (in red) generated along with their respective segmented images (in random color) after passing each volume slice through the Mask R-CNN trained model (M1).

<p>The image shows the predicted boundary boxes (in red) generated along with their respective segmented images (in random color) after passing each volume slice through the Mask R-CNN trained model (M1).</p>

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Bibliographic Details
Main Author:	Yubraj Gupta (7465394) (author)
Other Authors:	Rainer Heintzmann (112278) (author), Carlos Costa (410100) (author), Rui Jesus (20329654) (author), Eduardo Pinho (14312032) (author)
Published:	2024
Subjects:	Biochemistry Cell Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified weighted fusion technique slowing disease progression proposed fusion method produce adenosine triphosphate mean jaccard index mean error rate including neurodegenerative disorders data acquisition techniques containing numerous cells 3d cnn algorithms sem images using primary mitochondrial diseases xlink "> mitochondria efficiently segment mitochondria minimal user intervention overall segmentation results minimal intervention segmentation task pipelines using mitochondrial dna urocell dataset urgent need recent advances predicted outputs predict regions potentially aid pixel accuracy nutrition molecules methodology minimizes intracellular organelles interest specific individual predictions high score four datasets early detection dice coefficient deep learning cellular fuel become feasible automated tools article proposes architecture combines
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The image shows the predicted boundary boxes (in red) generated along with their respective segmented images (in random color) after passing each volume slice through the Mask R-CNN trained model (M1).

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