Pulmonary Vessel Segmentation Based on Orthogonal Fused U-Net++ of Chest CT Images

Cui, Hejie; Liu, Xinglong; Huang, Ning

doi:10.1007/978-3-030-32226-7_33

Cited by 39 publications

(24 citation statements)

References 14 publications

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“…The number of intermediary skip connection units depends on the layer number and decreases linearly when traversing the contracting path. Applications in U-net++ include segmentation of cell nuclei [73], cancer tissue [73], cardiac structures and vessels [74], [75], and pelvic organs [76].…”

Section: H U-net++mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: H U-net++mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…A. Vascular tree topology extraction 1) Vascular tree segmentation: The proposed method begin with vascular tree segmentation. Vascular trees are extracted from chest CT scan by fusion of vessels [15] and vessels near the hilum of the lung. The specific process is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction

Pan,

Zheng,

Huang

et al. 2021

Preprint

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With the development of medical computeraided diagnostic systems, pulmonary artery-vein(A/V) reconstruction plays a crucial role in assisting doctors in preoperative planning for lung cancer surgery. However, distinguishing arterial from venous irrigation in chest CT images remains a challenge due to the similarity and complex structure of the arteries and veins. We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images. The method consists of three parts. First, global connection information and local feature information are used to construct a complete topological tree and ensure the continuity of vessel reconstruction. Second, the multitask classification network proposed can automatically learn the differences between arteries and veins at different scales to reduce classification errors caused by changes in terminal vessel characteristics. Finally, the topology optimizer considers interbranch and intrabranch topological relationships to maintain spatial consistency to avoid the misclassification of A/V irrigations. We validate the performance of the method on chest CT images. Compared with manual classification, the proposed method achieves an average accuracy of 96.2% on noncontrast chest CT. In addition, the method has been proven to have good generalization, that is, the accuracies of 93.8% and 94.8% are obtained for CT scans from other devices and other modes, respectively. The result of pulmonary artery-vein reconstruction obtained by the proposed method can provide better assistance for preoperative planning of lung cancer surgery.

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“…The scSE block [23] further enhances feature representation via spatial channel pooling. Furthermore, many approaches are proposed to enhance semantic segmentation accuracy for particular medically relevant objects, in- cluding but not limited to liver lesion [2], surgical instruments [20, 19,18], pulmonary vessel [3], and lung tumor [15].…”

Section: Related Workmentioning

confidence: 99%

“…The iris annotations are then obtained by subtracting the convex-hull of the pupil segment from the cornea segment. This dataset contains 124 frames from 12 videos for training and 23 frames from two videos for testing 3 . For lens and pupil segmentation, we employ the two public datasets of the LensID framework [6], containing the annotation of the intraocular lens and pupil.…”

Section: Experimental Settingsmentioning

confidence: 99%

ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos

Ghamsarian¹,

Taschwer²,

Putzgruber-Adamitsch³

et al. 2021

Preprint

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Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as ReCal module, which can calibrate the feature maps by employing region intra-and-inter-dependencies and channel-region cross-dependencies. This calibration strategy can effectively enhance semantic representation by correlating different representations of the same semantic label, considering a multi-angle local view centering around each pixel. Thus the proposed module can deal with distant visual characteristics of unique objects as well as cross-similarities in the visual characteristics of different objects. Moreover, we propose a novel network architecture based on the proposed module termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net compared to rival state-of-the-art approaches for all relevant objects in cataract surgery. Moreover, ablation studies reveal the effectiveness of the ReCal module in boosting semantic segmentation accuracy.

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Pulmonary Vessel Segmentation Based on Orthogonal Fused U-Net++ of Chest CT Images

Cited by 39 publications

References 14 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction

ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos

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