ADMNet : Adaptive-Weighting Dual Mapping for Online Tracking With Respiratory Motion Estimation in Contrast-Enhanced Ultrasound
Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 2023 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
|---|---|
| Enthalten in: |
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society - 33(2023) vom: 21., Seite 58-68 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Ming-De [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 07.12.2023 Date Revised 07.12.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1109/TIP.2023.3333195 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM364795948 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM364795948 | ||
| 003 | DE-627 | ||
| 005 | 20231226100209.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/TIP.2023.3333195 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1215.xml |
| 035 | |a (DE-627)NLM364795948 | ||
| 035 | |a (NLM)37988213 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Ming-De |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a ADMNet |b Adaptive-Weighting Dual Mapping for Online Tracking With Respiratory Motion Estimation in Contrast-Enhanced Ultrasound |
| 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 07.12.2023 | ||
| 500 | |a Date Revised 07.12.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field | ||
| 650 | 4 | |a Journal Article | |
| 650 | 7 | |a Contrast Media |2 NLM | |
| 700 | 1 | |a Hu, Hang-Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Ruan, Si-Min |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Mei-Qing |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Li-Da |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Ze-Rong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Kuang, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Ming-De |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Qing-Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |d 1992 |g 33(2023) vom: 21., Seite 58-68 |w (DE-627)NLM09821456X |x 1941-0042 |7 nnns |
| 773 | 1 | 8 | |g volume:33 |g year:2023 |g day:21 |g pages:58-68 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/TIP.2023.3333195 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 33 |j 2023 |b 21 |h 58-68 | ||