IMIIN : An inter-modality information interaction network for 3D multi-modal breast tumor segmentation
Copyright © 2021 Elsevier Ltd. All rights reserved..
Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In clinical breast cancer analysis, experts often examine multi-modal images since such images provide abundant complementary information on tumor morphology. Known multi-modal breast tumor segmentation methods extracted 2D tumor features and used information from one modal to assist another. However, these methods were not conducive to fusing multi-modal information efficiently, or may even fuse interference information, due to the lack of effective information interaction management between different modalities. Besides, these methods did not consider the effect of small tumor characteristics on the segmentation results. In this paper, We propose a new inter-modality information interaction network to segment breast tumors in 3D multi-modal MRI. Our network employs a hierarchical structure to extract local information of small tumors, which facilitates precise segmentation of tumor boundaries. Under this structure, we present a 3D tiny object segmentation network based on DenseVoxNet to preserve the boundary details of the segmented tumors (especially for small tumors). Further, we introduce a bi-directional request-supply information interaction module between different modalities so that each modal can request helpful auxiliary information according to its own needs. Experiments on a clinical 3D multi-modal MRI breast tumor dataset show that our new 3D IMIIN is superior to state-of-the-art methods and attains better segmentation results, suggesting that our new method has a good clinical application prospect.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:95 |
|---|---|
| Enthalten in: |
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society - 95(2022) vom: 05. Jan., Seite 102021 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Peng, Chengtao [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Breast tumor segmentation |
|---|
| Anmerkungen: |
Date Completed 02.05.2022 Date Revised 02.05.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.compmedimag.2021.102021 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM333983742 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM333983742 | ||
| 003 | DE-627 | ||
| 005 | 20231225222751.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.compmedimag.2021.102021 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1113.xml |
| 035 | |a (DE-627)NLM333983742 | ||
| 035 | |a (NLM)34861622 | ||
| 035 | |a (PII)S0895-6111(21)00170-1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Peng, Chengtao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a IMIIN |b An inter-modality information interaction network for 3D multi-modal breast tumor segmentation |
| 264 | 1 | |c 2022 | |
| 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 02.05.2022 | ||
| 500 | |a Date Revised 02.05.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In clinical breast cancer analysis, experts often examine multi-modal images since such images provide abundant complementary information on tumor morphology. Known multi-modal breast tumor segmentation methods extracted 2D tumor features and used information from one modal to assist another. However, these methods were not conducive to fusing multi-modal information efficiently, or may even fuse interference information, due to the lack of effective information interaction management between different modalities. Besides, these methods did not consider the effect of small tumor characteristics on the segmentation results. In this paper, We propose a new inter-modality information interaction network to segment breast tumors in 3D multi-modal MRI. Our network employs a hierarchical structure to extract local information of small tumors, which facilitates precise segmentation of tumor boundaries. Under this structure, we present a 3D tiny object segmentation network based on DenseVoxNet to preserve the boundary details of the segmented tumors (especially for small tumors). Further, we introduce a bi-directional request-supply information interaction module between different modalities so that each modal can request helpful auxiliary information according to its own needs. Experiments on a clinical 3D multi-modal MRI breast tumor dataset show that our new 3D IMIIN is superior to state-of-the-art methods and attains better segmentation results, suggesting that our new method has a good clinical application prospect | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Breast tumor segmentation | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Inter-modality information interaction | |
| 650 | 4 | |a Multi-modal | |
| 700 | 1 | |a Zhang, Yue |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Bensheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Danny Z |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society |d 1996 |g 95(2022) vom: 05. Jan., Seite 102021 |w (DE-627)NLM012943649 |x 1879-0771 |7 nnns |
| 773 | 1 | 8 | |g volume:95 |g year:2022 |g day:05 |g month:01 |g pages:102021 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.compmedimag.2021.102021 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 95 |j 2022 |b 05 |c 01 |h 102021 | ||