TY - JOUR
T1 - CFFR-Net
T2 - A channel-wise features fusion and recalibration network for surgical instruments segmentation
AU - Mahmood, Tahir
AU - Hong, Jin Seong
AU - Ullah, Nadeem
AU - Lee, Sung Jae
AU - Wahid, Abdul
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/11
Y1 - 2023/11
N2 - Surgical instrument segmentation plays a crucial role in robot-assisted surgery by furnishing essential information about instrument location and orientation. This information not only enhances surgical planning but also augments the precision and safety of procedures. Despite promising strides in recent research on surgical instrument segmentation, accuracy still faces obstacles due to local feature processing limitations, surgical environment complexity, and instrument morphological variability. To address these challenges, we introduced the channel-wise features fusion and recalibration network (CFFR-Net). This network utilizes a dual-stream mechanism, combining a context-guided block and dense block for feature extraction. The context-guided block captures a variety of contextual information by using different dilation rates. Additionally, CFFR-Net employs a fusion mechanism that harmonizes context-guided and dense streams. This integration, along with the inclusion of Squeeze-and-Excitation attention, enhances both the precision and robustness of semantic instrument segmentation. We performed experiments using two publicly available datasets for surgical instrument segmentation: the Kvasir-instrument and Endovis2017 datasets. The results of these experiments were highly encouraging, as our proposed model exhibited remarkable performance on both datasets compared to the state-of-the-art methods. On the Kvasir-instrument set, our model achieved a Dice score of 95.84% and mean intersection over union (mIOU) value of 92.40%. Similarly, on the Endovis2017 set, it obtained a Dice score of 95.47% and mIOU value of 93.02%.
AB - Surgical instrument segmentation plays a crucial role in robot-assisted surgery by furnishing essential information about instrument location and orientation. This information not only enhances surgical planning but also augments the precision and safety of procedures. Despite promising strides in recent research on surgical instrument segmentation, accuracy still faces obstacles due to local feature processing limitations, surgical environment complexity, and instrument morphological variability. To address these challenges, we introduced the channel-wise features fusion and recalibration network (CFFR-Net). This network utilizes a dual-stream mechanism, combining a context-guided block and dense block for feature extraction. The context-guided block captures a variety of contextual information by using different dilation rates. Additionally, CFFR-Net employs a fusion mechanism that harmonizes context-guided and dense streams. This integration, along with the inclusion of Squeeze-and-Excitation attention, enhances both the precision and robustness of semantic instrument segmentation. We performed experiments using two publicly available datasets for surgical instrument segmentation: the Kvasir-instrument and Endovis2017 datasets. The results of these experiments were highly encouraging, as our proposed model exhibited remarkable performance on both datasets compared to the state-of-the-art methods. On the Kvasir-instrument set, our model achieved a Dice score of 95.84% and mean intersection over union (mIOU) value of 92.40%. Similarly, on the Endovis2017 set, it obtained a Dice score of 95.47% and mIOU value of 93.02%.
KW - Artificial intelligence
KW - Deep learning
KW - Semantic segmentation
KW - Surgical instruments segmentation
UR - http://www.scopus.com/inward/record.url?scp=85169897327&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2023.107096
DO - 10.1016/j.engappai.2023.107096
M3 - Article
AN - SCOPUS:85169897327
SN - 0952-1976
VL - 126
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 107096
ER -