PETS-Nets : Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks
Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
|---|---|
| Enthalten in: |
IEEE transactions on medical imaging - 43(2024), 3 vom: 29. März, Seite 1006-1017 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Pei, Yuchen [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 06.03.2024 Date Revised 06.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1109/TMI.2023.3327295 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM363669752 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM363669752 | ||
| 003 | DE-627 | ||
| 005 | 20240306232506.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/TMI.2023.3327295 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1318.xml |
| 035 | |a (DE-627)NLM363669752 | ||
| 035 | |a (NLM)37874705 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Pei, Yuchen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a PETS-Nets |b Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 06.03.2024 | ||
| 500 | |a Date Revised 06.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Zhao, Fenqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Laifa |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Lufan |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Zhengwang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, He |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Lisheng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Gang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE transactions on medical imaging |d 1982 |g 43(2024), 3 vom: 29. März, Seite 1006-1017 |w (DE-627)NLM082855269 |x 1558-254X |7 nnns |
| 773 | 1 | 8 | |g volume:43 |g year:2024 |g number:3 |g day:29 |g month:03 |g pages:1006-1017 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/TMI.2023.3327295 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 43 |j 2024 |e 3 |b 29 |c 03 |h 1006-1017 | ||