Qualitative comparison of segmentation results across different deep learning models for Lucchi++ slices.

Qualitative comparison of segmentation results across different deep learning models for Lucchi++ slices.

<p>The ground truth image of slice 32 is shown alongside predicted segmentations from all six models: Mask R-CNN, 3D U-Net, YoloV8, Ddrnet-23, Segformer-B2, and YoloV9.</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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