SPNet: Shape Prediction Using a Fully Convolutional Neural Network

Arif, Sheeraz; Knapp, Karen; Slabaugh, Greg

doi:10.1007/978-3-030-00928-1_49

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…A more promising direction would be to incorporate some shape or geometric constraints directly into the model to guide the segmentation process (e.g. Al Arif et al., 2018; Mesadi et al., 2018). Another possible improvement would be to use geometric features to segment higher level sections (e.g.…”

Section: Discussionmentioning

confidence: 99%

MAPHIS—Measuring arthropod phenotypes using hierarchical image segmentations

Mráz,

Štěpka,

Pekár

et al. 2023

Methods Ecol Evol

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Animal phenotypic traits are utilised in a variety of studies. Often the traits are measured from images. The processing of a large number of images can be challenging; nevertheless, image analytical applications, based on neural networks, can be an effective tool in automatic trait collection. Our aim was to develop a stand‐alone application to effectively segment an arthropod from an image and to recognise individual body parts: namely, head, thorax (or prosoma), abdomen and four pairs of appendages. It is based on convolutional neural network with U‐Net architecture trained on more than a thousand images showing dorsal views of arthropods (mainly of wingless insects and spiders). The segmentation model gave very good results, with the automatically generated segmentation masks usually requiring only slight manual adjustments. The application, named MAPHIS, can further (1) organise and preprocess the images; (2) adjust segmentation masks using a simple graphical editor; and (3) calculate various size, shape, colouration and pattern measures for each body part organised in a hierarchical manner. In addition, a special plug‐in function can align body profiles of selected individuals to match a median profile and enable comparison among groups. The usability of the application is shown in three practical examples. The application can be used in a variety of fields where measures of phenotypic diversity are required, such as taxonomy, ecology and evolution (e.g. mimetic similarity). Currently, the application is limited to arthropods, but it can be easily extended to other animal taxa.

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Section: Discussionmentioning

confidence: 99%

MAPHIS—Measuring arthropod phenotypes using hierarchical image segmentations

Mráz,

Štěpka,

Pekár

et al. 2023

Methods Ecol Evol

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“…The ASPP structure proposed in V2 was extended by using four cascaded convolutions with different sampling rates and Batch Normalization in parallel at the top layer of the feature mapping. • SPNET [31] first proposed the method of bandshaped pooling to capture the long-range relationship between isolated areas. In addition, a hybrid pooling module was designed to further model the advanced semantic information.…”

Section: Comparisons and Analysismentioning

confidence: 99%

PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing

Zhang,

Liu,

Liu

et al. 2024

J Cloud Comp

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In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.

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“…Lei et al [26] proposed a network based on adversarial consistency learning and dynamic convolution. Al Arif et al [27] used symbolic distance functions (SDFs) generated by modified U-Net instead of partition maps to obtain better topology prediction results. Furthermore, some researchers [28] [29] used autoencoder to constrain the shape of segmented targets.…”

Section: Related Workmentioning

confidence: 99%

SGU-Net: Shape-Guided Ultralight Network for Abdominal Image Segmentation

Lei

Sun

et al. 2023

IEEE J. Biomed. Health Inform.

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Convolutional neural networks (CNNs) have achieved significant success in medical image segmentation. However, they also suffer from the requirement of a large number of parameters, leading to a difficulty of deploying CNNs to low-source hardwares, e.g., embedded systems and mobile devices. Although some compacted or small memory-hungry models have been reported, most of them may cause degradation in segmentation accuracy. To address this issue, we propose a shape-guided ultralight network (SGU-Net) with extremely low computational costs. The proposed SGU-Net includes two main contributions: it first presents an ultralight convolution that is able to implement double separable convolutions simultaneously, i.e., asymmetric convolution and depthwise separable convolution. The proposed ultralight convolution not only effectively reduces the number of parameters but also enhances the robustness of SGU-Net. Secondly, our SGU-Net employs an additional adversarial shape-constraint to let the network learn shape representation of targets, which can significantly improve the segmentation accuracy for abdomen medical images using self-supervision. The SGU-Net is extensively tested on four public benchmark datasets, LiTS, CHAOS, NIH-TCIA and 3Dircbdb. Experimental results show that SGU-Net achieves higher segmentation accuracy using lower memory costs, and outperforms state-of-the-art networks. Moreover, we apply our ultralight convolution into a 3D volume segmentation network, which obtains a comparable performance with fewer parameters and memory usage. The available code of SGU-Net is released at https://github.com/SUST-reynole/SGUNet.

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SPNet: Shape Prediction Using a Fully Convolutional Neural Network

Cited by 18 publications

References 12 publications

MAPHIS—Measuring arthropod phenotypes using hierarchical image segmentations

MAPHIS—Measuring arthropod phenotypes using hierarchical image segmentations

PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing

SGU-Net: Shape-Guided Ultralight Network for Abdominal Image Segmentation

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