3D segmentation of MDCK CYSTS
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PROVIDER: S-BIAD1070 | bioimages |
REPOSITORIES: bioimages
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Project description:BackgroundCell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has received much less attention. 3D segmentation faces significant challenges, including limited training data availability due to the difficulty of the task for human annotators, and inherent three-dimensional complexity. As a result, existing 3D cell segmentation methods often lack broad applicability across different imaging modalities.ResultsTo address this, we developed a generalizable approach for using 2D cell segmentation methods to produce accurate 3D cell segmentations. We implemented this approach in 3DCellComposer, a versatile, open-source package that allows users to choose any existing 2D segmentation model appropriate for their tissue or cell type(s) without requiring any additional training. Importantly, we have enhanced our open source CellSegmentationEvaluator quality evaluation tool to support 3D images. It provides metrics that allow selection of the best approach for a given imaging source and modality, without the need for human annotations to assess performance. Using these metrics, we demonstrated that our approach produced high-quality 3D segmentations of tissue images, and that it could outperform an existing 3D segmentation method on the cell culture images with which it was trained.Conclusions3DCellComposer, when paired with well-trained 2D segmentation models, provides an important alternative to acquiring human-annotated 3D images for new sample types or imaging modalities and then training 3D segmentation models using them. It is expected to be of significant value for large scale projects such as the Human BioMolecular Atlas Program.
| S-EPMC10979887 | biostudies-literature
Project description:Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.
| S-EPMC3360689 | biostudies-other
Project description:BackgroundSpatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice.MethodsTo address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology.ResultsWe evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics.ConclusionsThe proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
| S-EPMC10724925 | biostudies-literature
Project description:Fibrous scaffolds have been extensively used in three-dimensional (3D) cell culture systems to establish in vitro models in cell biology, tissue engineering, and drug screening. It is a common practice to characterize cell behaviors on such scaffolds using confocal laser scanning microscopy (CLSM). As a noninvasive technology, CLSM images can be utilized to describe cell-scaffold interaction under varied morphological features, biomaterial composition, and internal structure. Unfortunately, such information has not been fully translated and delivered to researchers due to the lack of effective cell segmentation methods. We developed herein an end-to-end model called Aligned Disentangled Generative Adversarial Network (AD-GAN) for 3D unsupervised nuclei segmentation of CLSM images. AD-GAN utilizes representation disentanglement to separate content representation (the underlying nuclei spatial structure) from style representation (the rendering of the structure) and align the disentangled content in the latent space. The CLSM images collected from fibrous scaffold-based culturing A549, 3T3, and HeLa cells were utilized for nuclei segmentation study. Compared with existing commercial methods such as Squassh and CellProfiler, our AD-GAN can effectively and efficiently distinguish nuclei with the preserved shape and location information. Building on such information, we can rapidly screen cell-scaffold interaction in terms of adhesion, migration and proliferation, so as to improve scaffold design.
| S-EPMC8852265 | biostudies-literature
Project description:Motivation3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task.ResultsIn this article, we propose a 3D wavelet and deep learning-based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction.Availabilityand implementationThe data and codes for this work are available at https://github.com/LiQiufu/3D-WaveUNet.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8756182 | biostudies-literature
Project description:Recognizing material categories is one of the core challenges in robotic nuclear waste decommissioning. All nuclear waste should be sorted and segregated according to its materials, and then different disposal post-process can be applied. In this paper, we propose a novel transfer learning approach to learn boundary-aware material segmentation from a meta-dataset and weakly annotated data. The proposed method is data-efficient, leveraging a publically available dataset for general computer vision tasks and coarsely labeled material recognition data, with only a limited number of fine pixel-wise annotations required. Importantly, our approach is integrated with a Simultaneous Localization and Mapping (SLAM) system to fuse the per-frame understanding delicately into a 3D global semantic map to facilitate robot manipulation in self-occluded object heaps or robot navigation in disaster zones. We evaluate the proposed method on the Materials in Context dataset over 23 categories and that our integrated system delivers quasi-real-time 3D semantic mapping with high-resolution images. The trained model is also verified in an industrial environment as part of the EU RoMaNs project, and promising qualitative results are presented. A video demo and the newly generated data can be found at the project website (Supplementary Material).
| S-EPMC7805863 | biostudies-literature
Project description:Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.
| S-EPMC10948387 | biostudies-literature
Project description:The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were acquired from 117 subjects from two institutions, which were manually segmented to generate the ground truth. Semantic segmentation was performed using basic 3D U-Net and a cascaded 3D U-Net. A stress test was performed using different sets of condylar images as the training, validation, and test datasets. Relative accuracy was evaluated using dice similarity coefficients (DSCs) and Hausdorff distance (HD). In the five stages, the DSC ranged 0.886-0.922 and 0.912-0.932 for basic 3D U-Net and cascaded 3D U-Net, respectively; the HD ranged 2.557-3.099 and 2.452-2.600 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage V (largest data from two institutions) exhibited the highest DSC of 0.922 ± 0.021 and 0.932 ± 0.023 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage IV (200 samples from two institutions) had a lower performance than stage III (162 samples from one institution). Our results show that fully automated segmentation of mandibular condyles is possible using 3D U-Net algorithms, and the segmentation accuracy increases as training data increases.
| S-EPMC9709043 | biostudies-literature
Project description:BackgroundReliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.ResultsBoth qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.ConclusionThe proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.
| S-EPMC2064921 | biostudies-literature
Project description:Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.
| S-EPMC4917178 | biostudies-literature