Precision Liver Resection: Three-Dimensional Reconstruction Combined with Fluorescence Laparoscopic Imaging

Ni, Zhongkai; Lin, Dongdong; Wang, Ziqiang; Jin, Haimin; Li, Xiaowen; Li, Ye; Huang, Hai

doi:10.1177/1553350620954581

Cited by 18 publications

(16 citation statements)

References 19 publications

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“…He also pioneered new ideas of using imaging tools to study anatomy. At present, 3D reconstruction technology is being replicated in the implementation of clinical surgeries (21)(22)(23). According to According to the distribution of main branches V 1 and V 2 , Shimizu (11) divided the right SPVs into the anterior with central Iab type (V 1 a+V 1 b located anterior to B 3 , and V 2 a+V 2 b+V 2 c located posterior to B 3 , 54%), anterior with central Ib type (only V 1 b located anterior to B 3 , and V 1 a+V 2 a+V 2 b+V 2 c located posterior to B 3 , 26%), central type (V 1 a+V 1 b+V 2 a+V 2 b+V 2 c all located posterior to B 3 , 7%) and anterior type (V 1 a+V 1 b+VX 2 a located anterior to B 3 , V 2 b+V 2 c located posterior to B 3 , 12%).…”

Section: Discussionmentioning

confidence: 99%

A modified system for classifying the bilateral superior pulmonary veins using three-dimensional computed tomography bronchography and angiography images

Wang¹,

Lin²,

Bian³

et al. 2021

J Thorac Dis

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Background: Identifying the distribution of pulmonary veins with three-dimensional reconstruction images is of great significance for surgical guidance. Existing models neglect the consistency of the bilateral superior pulmonary veins (SPVs) and lack a simple unified classification pattern. This study aimed to analyze the distributional features of bilateral SPVs, based on a cohort of patients undergoing CT examination. Methods: The three-dimensional computed tomography bronchography and angiography (3D-CTBA) images of 1,520 cases were retrospectively analyzed. The reconstructed images of the right upper lobes were read in 715 cases, and left upper lobes in 805 cases. Through symmetrical analysis, the circulation of main venous branches and the spatial relationships of confluences with adjacent bronchus were compared. Results: The SPVs of bilateral upper lobes showed common distributional features and were divided into three main types. The central vein type, the semi-central vein type, and the non-central vein type accounted for 83.35% [596], 7.84% [56], 8.11% [58] of the 715 cases with right scanning, and 25.71% [207], 62.61% [504], 10.81% [87] of the 805 cases with left scanning, respectively. There were 5 (0.70%) cases with rare variations in the right upper lobe and 7 (0.87%) in the left upper lobe. The attribution of intersubsegmental vein in the posterior segment (V 2 b) and its position relative to the anterior segmental bronchus (B 3 ) was the basis of classification in the right upper lobe, and the attribution of intersubsegmental vein in the apicoposterior segment (V 1+2 c) and its position relative to B 3 was the basis of classification in the left upper lobe. In this classification system, the branching pattern of the intersegmental vein between the apical segment and the anterior segment (V 1 b) in the right upper lobe, and the intersegmental vein between the apicoposterior segment and the anterior segment (V 1+ 2 a) in the left upper lobe were used for subdivision.Conclusions: Our modified system had a high degree of consistency in classifying SPVs in bilateral upper lobes, thus providing guidance for preoperative and intraoperative procedures.

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Section: Discussionmentioning

confidence: 99%

A modified system for classifying the bilateral superior pulmonary veins using three-dimensional computed tomography bronchography and angiography images

Wang¹,

Lin²,

Bian³

et al. 2021

J Thorac Dis

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“…The precautions for laparoscopic anatomical resection of liver S8 are listed as follows: (1) Preoperative and intraoperative high resolution thin-sliced enhanced CT scanning, helical CT arterial portography and 3D reconstruction visualization system can be used to make accurate assessments of the location and courses of Glissonean pedicles, MHV, and RHV in S8 to avoid damaging the vessels that need to be preserved during surgery [19]. (2) This technique is not dependent on liver staining by injection of special dye.…”

Section: Discussionmentioning

confidence: 99%

Laparoscopic Anatomical Resection of Liver Segment 8 via a Hepatic Parenchymal Transection-first Approach Guided by the Middle Hepatic Vein

You

et al. 2021

Preprint

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Background Although recent technological developments and improved endoscopic procedures have further extended the application of laparoscopic liver resection, pure laparoscopic anatomic resection of liver segment 8 (S8) is still rarely performed due to the lack of an appropriate surgical approach. This article discusses the technical tips and operation methods for laparoscopic anatomical resection of liver S8 via a hepatic parenchymal transection-first approach.Methods Clinical data of 14 patients who underwent laparoscopic anatomical resection of liver segment 8 via a hepatic parenchymal transection-first approach guided by the middle hepatic vein (MHV) in the Second Affiliated Hospital, Third Military Medical University (Army Medical University) from May 2017 to December 2019 were retrospectively analyzed. The operation time, intraoperative blood loss, postoperative complications, and hospitalization duration were observed.Results The operation was successful with no complications. No other abnormality was noted during outpatient follow-up examination.Conclusions Laparoscopic anatomical resection of liver S8 is still quite challenging at present, and it is our goal to design a reasonable procedure with accurate efficacy and high safety. We use hepatic parenchymal transection-first approach guided by the MHV for laparoscopic anatomical resection of liver S8. This technique overcomes the problem of high technical risk, greatly reduces the surgical difficulty and achieves technological breakthroughs, but there are still many problems worth further exploration.

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“…Many of the existing benchmarks [61], [71] only use the region-based measurement, Dice similarity coefficient (DSC), to evaluate and rank segmentation methods. However, it is insufficient to measure the boundary accuracy as demonstrated and analyzed in Figure 4, and boundary accuracy is also crucial in clinical practice [72], [73].…”

Section: Limitations Of Existing Abdominal Organ Segmentation Methods and Benchmark Datasetsmentioning

confidence: 99%

“…', we conduct a comprehensive study of the SOTA abdominal organ segmentation method (nnU-Net [33]) on the AbdomenCT-1K dataset for single organ and multi-organ segmentation tasks. In addition, we introduce the normalized surface Dice (NSD) [74] as an additional evaluation metric because the segmentation accuracy in organ boundaries is also very important [72], [73] in clinical practice.…”

Section: Contributionsmentioning

confidence: 99%

“…However, the DSC value is still very high that can not well reflect the boundary error, while NSD is relative lower that is sensitive to this boundary error. In many clinical tasks, such as preoperative planning and organ transplant, boundary errors are very critical [72], [73] and thus should be eliminated. Another benefit of introducing NSD is that it ignores small boundary deviations because small inter-observer errors are also unavoidable and often not clinically relevant when segmenting the organs by radiologists.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

Zhang

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

215

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With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i.e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets. However, most of the existing abdominal organ segmentation benchmark datasets only contain single-center, single-phase, single-vendor, or single-disease cases, thus, it is unclear whether the excellent performance can generalize on more diverse datasets. In this paper, we present a large and diverse abdominal CT organ segmentation dataset, termed as AbdomenCT-1K, with more than 1000 (1K) CT scans from 11 countries, including multi-center, multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation, as well as reveal the unsolved segmentation problems of the SOTA method, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the introduction of the AbdomenCT-1K dataset will promote the future in-depth research towards clinical applicable abdominal organ segmentation methods. Moreover, the datasets, codes and trained models of baseline methods will be publicly available.

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Precision Liver Resection: Three-Dimensional Reconstruction Combined with Fluorescence Laparoscopic Imaging

Cited by 18 publications

References 19 publications

A modified system for classifying the bilateral superior pulmonary veins using three-dimensional computed tomography bronchography and angiography images

A modified system for classifying the bilateral superior pulmonary veins using three-dimensional computed tomography bronchography and angiography images

Laparoscopic Anatomical Resection of Liver Segment 8 via a Hepatic Parenchymal Transection-first Approach Guided by the Middle Hepatic Vein

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

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