Two-stage Segmentation Framework Research Articles

Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation

The phenotypic characteristics of soybean leaves are of great significance for studying the growth status, physiological traits, and response to the environment of soybeans. The segmentation model for soybean leaves plays a crucial role in morphological analysis. However, current baseline segmentation models are unable to accurately segment leaves in soybean leaf images due to issues like leaf overlap. In this paper, we propose a target leaf segmentation model based on leaf localization and guided segmentation. The segmentation model adopts a two-stage segmentation framework. The first stage involves leaf detection and target leaf localization. Based on the idea that a target leaf is close to the center of the image and has a relatively large area, we propose a target leaf localization algorithm. We also design an experimental scheme to provide optimal localization parameters to ensure precise target leaf localization. The second stage utilizes the target leaf localization information obtained from the first stage to guide the segmentation of the target leaf. To reduce the dependency of the segmentation results on the localization information, we propose a solution called guidance offset strategy to improve segmentation accuracy. We design multiple guided model experiments and select the one with the highest segmentation accuracy. Experimental results demonstrate that the proposed model exhibits strong segmentation capabilities, with the highest average precision (AP) and average recall (AR) reaching 0.976 and 0.981, respectively. We also compare our segmentation results with current baseline segmentation models, and multiple quantitative indicators and qualitative analysis indicate that our segmentation results are better.

Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networks.

Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OARdelineation. Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high-performance method for automatic segmentation of 10 OARs required in radiotherapy planning for braintumors. A novel two-stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High- and Low-Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high-resolution branch is introduced to segment medium-sized organs, and a High-Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimalglands. Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state-of-the-art models including nnU-Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium-sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs oforgans. Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium-sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentationaccuracy.

Extension–contraction transformation network for pancreas segmentation in abdominal CT scans

Accurate and automatic pancreas segmentation from abdominal computed tomography (CT) scans is crucial for the diagnosis and prognosis of pancreatic diseases. However, the pancreas accounts for a relatively small portion of the scan and presents high anatomical variability and low contrast, making traditional automated segmentation methods fail to generate satisfactory results. In this paper, we propose an extension–contraction transformation network (ECTN) and deploy it into a cascaded two-stage segmentation framework for accurate pancreas segmenting. This model can enhance the perception of 3D context by distinguishing and exploiting the extension and contraction transformation of the pancreas between slices. It consists of an encoder, a segmentation decoder, and an extension–contraction (EC) decoder. The EC decoder is responsible for predicting the inter-slice extension and contraction transformation of the pancreas by feeding the extension and contraction information generated by the segmentation decoder; meanwhile, its output is combined with the output of the segmentation decoder to reconstruct and refine the segmentation results. Quantitative evaluation is performed on NIH Pancreas Segmentation (Pancreas-CT) dataset using 4-fold cross-validation. We obtained average Precision of 86.59±6.14% , Recall of 85.11±5.96%, Dice similarity coefficient (DSC) of 85.58±3.98%. and Jaccard Index (JI) of 74.99±5.86%. The performance of our method outperforms several baseline and state-of-the-art methods.

A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS

Abstract. Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods. This paper proposes a two-stage segmentation framework with automated workflows for imbalanced rare classes based on general semantic segmentation. The proposed approach includes two stages: general semantic segmentation and object-based refined semantic segmentation. Firstly, general segmentation networks are utilized to segment general large objects. Secondly, refined semantic segmentation is conducted by an automated workflow: 3D clustering and bounding box (BBox) generation are applied to the point cloud of rare fine-grained objects during the training, followed by object detection to extract fine-grained objects. Afterwards, as the constraints, the extracted BBoxes further refine the segmentation results. Our approach is evaluated on the Hessigheim High-Resolution 3D Point Cloud (H3D) Benchmark and obtains state-of-the-art 89.35% overall accuracy and outstanding 75.70% mean F1-Score. Furthermore, rare classes Vehicle and Chimney achieve breakthroughs from zero to 63.63% and 52.00% in F1-score, respectively.

Two-Stage Segmentation Framework Based on Distance Transformation.

With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in medical images, making it challenging to achieve high-accuracy segmentation of lesions or organs. In this paper, we assume that the distance map obtained by performing distance transformation on the ROI edge can be used as a weight map to make the network pay more attention to the learning of the ROI edge region. To this end, we design a novel framework to flexibly embed the distance map into the two-stage network to improve left atrium MRI segmentation performance. Furthermore, a series of distance map generation methods are proposed and studied to reasonably explore how to express the weight of assisting network learning. We conduct thorough experiments to verify the effectiveness of the proposed segmentation framework, and experimental results demonstrate that our hypothesis is feasible.

SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation.

Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

A weighted bounded Hessian variational model for image labeling and segmentation

Natural images are usually composed of multiple objects at different scales in flat and slanted regions. Traditional labeling/segmentation approaches based on total variation minimization may produce staircase results with discontinuities and rough boundaries. In this paper, we propose a novel weighted variational model for image labeling/segmentation in the space of functions of bounded Hessian, the weights of which are automatically estimated based on edge information of the observed images. Especially, by minimizing the combined first and second-order regularizer, our model can overcome the shortage of total variation and provide more meaningful results. The efficient alternating direction method of multipliers based algorithm is established, all subproblems of which can be solved by either the fast Fourier transform or closed-form solution. We further introduce the weighted bounded Hessian regularizer into the two-stage segmentation framework for dealing with noisy and blurry image segmentation problems. Numerous experiments are conducted on both two-phase and multi-phase labeling/segmentation problems. By comparing with several state-of-the-art methods both qualitatively and quantitatively, it demonstrates that the proposed models can prominently improve the accuracy of image labeling and segmentation.

Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one.