PMID- 38265915
OWN - NLM
STAT- Publisher
LR  - 20240124
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - PP
DP  - 2024 Jan 24
TI  - Few-Shot Medical Image Segmentation via Generating Multiple Representative 
      Descriptors.
LID - 10.1109/TMI.2024.3358295 [doi]
AB  - Automatic medical image segmentation has witnessed significant development with 
      the success of large models on massive datasets. However, acquiring and 
      annotating vast medical image datasets often proves to be impractical due to the 
      time consumption, specialized expertise requirements, and compliance with patient 
      privacy standards, etc. As a result, Few-shot Medical Image Segmentation (FSMIS) 
      has become an increasingly compelling research direction. Conventional FSMIS 
      methods usually learn prototypes from support images and apply nearest-neighbor 
      searching to segment the query images. However, only a single prototype cannot 
      well represent the distribution of each class, thus leading to restricted 
      performance. To address this problem, we propose to Generate Multiple 
      Representative Descriptors (GMRD), which can comprehensively represent the 
      commonality within the corresponding class distribution. In addition, we design a 
      Multiple Affinity Maps based Prediction (MAMP) module to fuse the multiple 
      affinity maps generated by the aforementioned descriptors. Furthermore, to 
      address intra-class variation and enhance the representativeness of descriptors, 
      we introduce two novel losses. Notably, our model is structured as a dual-path 
      design to achieve a balance between foreground and background differences in 
      medical images. Extensive experiments on four publicly available medical image 
      datasets demonstrate that our method outperforms the state-of-the-art methods, 
      and the detailed analysis also verifies the effectiveness of our designed module.
FAU - Cheng, Ziming
AU  - Cheng Z
FAU - Wang, Shidong
AU  - Wang S
FAU - Xin, Tong
AU  - Xin T
FAU - Zhou, Tao
AU  - Zhou T
FAU - Zhang, Haofeng
AU  - Zhang H
FAU - Shao, Ling
AU  - Shao L
LA  - eng
PT  - Journal Article
DEP - 20240124
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
EDAT- 2024/01/24 18:42
MHDA- 2024/01/24 18:42
CRDT- 2024/01/24 12:23
PHST- 2024/01/24 18:42 [medline]
PHST- 2024/01/24 18:42 [pubmed]
PHST- 2024/01/24 12:23 [entrez]
AID - 10.1109/TMI.2024.3358295 [doi]
PST - aheadofprint
SO  - IEEE Trans Med Imaging. 2024 Jan 24;PP. doi: 10.1109/TMI.2024.3358295.