Vessel delineation using U-Net: A sparse labeled deep learning approach for semantic segmentation of histological images

to connected objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: 10.3390/cancers15153773

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2023

Creator: Glänzer, Lukas
Masalkhi, Husam E.
Röth, Anjali Anna Joy
Schmitz-Rode, Thomas
Slabu, Ioana

DOI: 10.18154/RWTH-2023-07337

URN: urn:nbn:de:101:1-2023080401362212121391

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:44 PM CET

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Associated

Glänzer, Lukas
Masalkhi, Husam E.
Röth, Anjali Anna Joy
Schmitz-Rode, Thomas
Slabu, Ioana
Universitätsbibliothek der RWTH Aachen

Time of origin

2023

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Automatic segmentation of the Vessel lumen from 3D CTA images of aortic dissection

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Learning Instance Segmentation from Sparse Supervision

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Film

Vessel

Atlas

Histological typing of intestinal tumours

Histological typing of endocrine tumours

Histological typing of ovarian tumours

Learning Instance Segmentation from Sparse Supervision

Robust Vessel Segmentation in Fundus Images

Hochschulschrift

Statistical learning techniques for text categorization with sparse labeled data

Global and local sparse subspace optimization for motion segmentation

MISM: a medical image segmentation metric for evaluation of weak labeled data

Histological techniques

Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks

Hochschulschrift

Automatic segmentation of the Vessel lumen from 3D CTA images of aortic dissection

Film

Vessel

Atlas

Histological typing of intestinal tumours

Histological typing of endocrine tumours

Histological typing of ovarian tumours

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Vessel delineation using U-Net: A sparse labeled deep learning approach for semantic segmentation of histological images

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Other Objects (12)

Learning Instance Segmentation from Sparse Supervision

Robust Vessel Segmentation in Fundus Images

Statistical learning techniques for text categorization with sparse labeled data

Global and local sparse subspace optimization for motion segmentation

MISM: a medical image segmentation metric for evaluation of weak labeled data

Histological techniques

Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks

Automatic segmentation of the Vessel lumen from 3D CTA images of aortic dissection

Vessel

Histological typing of intestinal tumours

Histological typing of endocrine tumours

Histological typing of ovarian tumours

Learning Instance Segmentation from Sparse Supervision

Robust Vessel Segmentation in Fundus Images

Statistical learning techniques for text categorization with sparse labeled data

Global and local sparse subspace optimization for motion segmentation

MISM: a medical image segmentation metric for evaluation of weak labeled data

Histological techniques

Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks

Automatic segmentation of the Vessel lumen from 3D CTA images of aortic dissection

Vessel

Histological typing of intestinal tumours

Histological typing of endocrine tumours

Histological typing of ovarian tumours

Learning Instance Segmentation from Sparse Supervision

Robust Vessel Segmentation in Fundus Images

Statistical learning techniques for text categorization with sparse labeled data

Global and local sparse subspace optimization for motion segmentation

MISM: a medical image segmentation metric for evaluation of weak labeled data

Histological techniques

Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks

Automatic segmentation of the Vessel lumen from 3D CTA images of aortic dissection

Vessel

Histological typing of intestinal tumours

Histological typing of endocrine tumours

Histological typing of ovarian tumours

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