35 research outputs found

    Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions

    Full text link
    Segmentation of pathological images is a crucial step for accurate cancer diagnosis. However, acquiring dense annotations of such images for training is labor-intensive and time-consuming. To address this issue, Semi-Supervised Learning (SSL) has the potential for reducing the annotation cost, but it is challenged by a large number of unlabeled training images. In this paper, we propose a novel SSL method based on Cross Distillation of Multiple Attentions (CDMA) to effectively leverage unlabeled images. Firstly, we propose a Multi-attention Tri-branch Network (MTNet) that consists of an encoder and a three-branch decoder, with each branch using a different attention mechanism that calibrates features in different aspects to generate diverse outputs. Secondly, we introduce Cross Decoder Knowledge Distillation (CDKD) between the three decoder branches, allowing them to learn from each other's soft labels to mitigate the negative impact of incorrect pseudo labels in training. Additionally, uncertainty minimization is applied to the average prediction of the three branches, which further regularizes predictions on unlabeled images and encourages inter-branch consistency. Our proposed CDMA was compared with eight state-of-the-art SSL methods on the public DigestPath dataset, and the experimental results showed that our method outperforms the other approaches under different annotation ratios. The code is available at \href{https://github.com/HiLab-git/CDMA}{https://github.com/HiLab-git/CDMA.}Comment: Provisional Accepted by MICCAI 202

    Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks

    Full text link
    We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory requirements can be adjusted based on the available resources. We optimized MedicDeepLabv3+ and six other state-of-the-art convolutional neural networks (DeepLabv3+, UNet, HighRes3DNet, V-Net, VoxResNet, Demon) on a heterogeneous training set comprised by MR volumes from 11 cohorts acquired at different lesion stages. Then, we evaluated the trained models and two approaches specifically designed for rodent MRI skull stripping (RATS and RBET) on a large dataset of 655 MR rat brain volumes. In our experiments, MedicDeepLabv3+ outperformed the other methods, yielding an average Dice coefficient of 0.952 and 0.944 in the brain and contralateral hemisphere regions. Additionally, we show that despite limiting the GPU memory and the training data, our MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus, yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rat neuroimaging studies.Comment: Published in NeuroInformatic

    TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

    Get PDF
    Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods

    Computational Histopathology Analysis based on Deep Learning

    Get PDF
    Pathology has benefited from the rapid progress in technology of digital scanning during the last decade. Nowadays, slide scanners are able to produce super-resolution whole slide images (WSI), also called digital slides, which can be explored by image viewers as an alternative to the use of conventional microscope. The use of WSI together with the other microscopic and molecular pathology images brings the development of digital pathology, which further enables to perform digital diagnostics. Moreover, the availability of WSI makes it possible to apply image processing and recognition techniques to support digital diagnostics, opening new revenues of computational pathology. However, there still remain many challenging tasks towards computational pathology such as automated cancer categorisation, tumour area segmentation, and cell-level instance detection. In this study, we explore problems related to the above tasks in histology images. Cancer categorisation can be addressed as a histopathological image classification problem. Multiple aspects such as variations caused by magnification factors and class imbalance make it a challenging task where conventional methods cannot obtain satisfactory performance in many cases. We propose to learn similarity-based embeddings for magnification-independent cancer categorisation. A pair loss and a triplet loss are proposed to learn embeddings that can measure similarity between images for classification. Furthermore, to eliminate the impact of class imbalance, instead of using the strategy of hard samples mining that intuitively discard some easy samples, we introduce a new loss function to simultaneously punish hard misclassified samples and suppress easy well-classified samples. Tumour area segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. Vague boundaries and small regions dissociated from viable tumour areas are two main challenges to accurately segment tumour area. We present a structure-aware scale-adaptive feature selection method for efficient and accurate tumour area segmentation. Specifically, based on a segmentation network with a popular encoder-decoder architecture, a scale-adaptive module is proposed to select more robust features to represent the vague, non-rigid boundaries. Furthermore, a structural similarity metric is proposed for better tissue structure awareness to deal with small region segmentation. Detection of cell-level instances in histology images is essential to acquire morphological and numeric clues for cancer assessment. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. We propose similarity-based region proposal networks for nuclei and cells detection in histology images. In particular, a customized convolution layer termed as embedding layer is designed for network building. The embedding layer is then added on to modify the region proposal networks, which enables the networks to learn discriminative features based on similarity learning

    Rock-typing and permeability estimation of thin-bedded reservoir rock by NMR in the presence of diffusion coupling

    Full text link
    Conventional interpretation approaches to nuclear magnetic resonance (NMR) measurements of fluid-saturated reservoir rock rely on the assumption that the distributions of transverse relaxation time (T2) and pore size directly correlate. In practical scenarios this assumption is found not to apply in numerous multi-scale porosity structures as a result of what is known as “diffusion coupling” occurring between various pores. This problem has been analyzed in the context of individual pores, but less so for larger regions of interest. For gas reservoirs in particular it arises frequently in the case of thinly laminated reservoirs due to their characteristically small layer thickness and the subsequent shorter distances to be covered by mobile spins and the appreciably higher diffusion coefficients that characterize gas reservoirs. In such instances, rock-typing cannot directly be achieved from NMR measurements. This study employs NMR simulations on tomographic images for the interpretation of NMR measurements in the presence of interbed diffusion coupling. Knowledge about the magnetization decay of the coupling region is used together with prior knowledge of the individual rock types forming the layered rocks in a methodical treatment to establish the coupling strength (ξ_R). Following successful completion of rock-typing, the improvement in the estimation of vertical and horizontal permeabilities was evaluated, which relies on a proper definition of T2lm for each rock-type that corrected for diffusion coupling. The Lattice-Boltzmann (LB) method was also used to assess the enhancements in the NMR permeability estimations. Synthetic consolidated and unconsolidated laminated structures with two distinct grain sizes and various layer thicknesses are used to test the approach both numerically and experimentally. A relationship between strengthening pore coupling and reducing bed thickness was noted, together with the increase in the diffusion coefficient and the decrease in surface relaxivity. In instances of strong pore coupling, the T2 distribution was found to inaccurately represent the inherent bimodal distribution relative to various morphologies. Successful rock-typing was attained through the decoupling procedure by applying the value of (ξ_R) that consequently improve the NMR permeability estimation

    Comparative anatomy and evolution of the gastrotrich muscular system

    Get PDF
    Gastrotrichs figure prominently in metazoan phylogeny because they share a suite of complex morphological characteristics with several other members of the Bilateria. But their microscopic size, cryptic interstitial habitat, and lack of fossil record have exacerbated the usual barriers to phylogenetic analysis. To arrive at a better understanding of gastrotrich systematics and evolution, cladistic analyses and detailed studies of the muscular system were performed. A fluorescent F-actin stain was applied to whole mounts of 26 species of Gastrotricha to characterize the musculature. Muscle patterns were mapped, their functions inferred, and the direction of evolution hypothesized for several families. The musculature of all gastrotrichs is arranged as a series of circular, helicoidal, and longitudinal bands around the digestive tract. Circular muscles are generally present in splanchnic and somatic positions. Helicoidal muscles in 50--60° angles are present on the pharynx and intestine of most species. Longitudinal muscles are arranged radially around the digestive tract in dorsal, lateral, ventral and ventrolateral positions. Extraordinary muscle orientations are present in several species. In macrodasyidan gastrotrichs, the musculature of Dactylopodola baltica (Dactylopodolidae) is considered to be closest to the ground pattern of the phylum and consists of the following: splanchnic circular muscles on the pharynx and intestine, longitudinal muscles in dorsal, lateral, ventral and ventrolateral positions, pharyngeal and intestinal helicoidal muscles, and somatic circular muscles. Within the Chaetonotida, species of Neodasys and Xenotrichula have the most plesiomorphic muscle topologies. Muscle patterns are similar to macrodasyidans though several muscle orientations have become reduced (splanchnic and somatic circular muscles), are the result of evolutionary modification to existing muscles (incomplete splanchnic and somatic circular muscles, dorsoventral muscles) or evolved independently (the branched Ruckenhautmuskel). This study relied on a phylogenetic perspective to delineate the origin of specific muscle patterns in gastrotrichs and allow for the separation of phyletic heritage from adaptation. Several species from both orders possess muscle patterns that can be regarded as apomorphic and may therefore serve as taxonomic characters. Closer scrutiny of these species may reveal the underlying selective processes that led to the origin and maintenance of novel muscle orientations in gastrotrichs
    corecore