The image displays the comparison between the predicted output of the 3D U-Net model (shown in the green channel) and the ground-truth image (shown in the red channel) for various datasets.

<p>The yellow-colored regions indicate the true positive regions where the 3D U-Net model accurately segmented most ROIs. Overall, the performance of the 3D U-Net model is relatively good compared to the predictions from Mask R-CNN. However, there are still instances, such as in slice number 5...

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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 displays the comparison between the predicted output of the 3D U-Net model (shown in the green channel) and the ground-truth image (shown in the red channel) for various datasets.

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