3,050 research outputs found
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
We propose HookNet, a semantic segmentation model for histopathology
whole-slide images, which combines context and details via multiple branches of
encoder-decoder convolutional neural networks. Concentricpatches at multiple
resolutions with different fields of view are used to feed different branches
of HookNet, and intermediate representations are combined via a hooking
mechanism. We describe a framework to design and train HookNet for achieving
high-resolution semantic segmentation and introduce constraints to guarantee
pixel-wise alignment in feature maps during hooking. We show the advantages of
using HookNet in two histopathology image segmentation tasks where tissue type
prediction accuracy strongly depends on contextual information, namely (1)
multi-class tissue segmentation in breast cancer and, (2) segmentation of
tertiary lymphoid structures and germinal centers in lung cancer. Weshow the
superiority of HookNet when compared with single-resolution U-Net models
working at different resolutions as well as with a recently published
multi-resolution model for histopathology image segmentatio
GAN-Based Super-Resolution And Segmentation Of Retinal Layers In Optical Coherence Tomography Scans
Optical Coherence Tomography (OCT) has been identified as a noninvasive and cost-effective imaging modality for identifying potential biomarkers for Alzheimer\u27s diagnosis and progress detection. Current hypotheses indicate that retinal layer thickness, which can be assessed via OCT scans, is an efficient biomarker for identifying Alzheimer\u27s disease. Due to factors such as speckle noise, a small target region, and unfavorable imaging conditions manual segmentation of retina layers is a challenging task. Therefore, as a reasonable first step, this study focuses on automatically segmenting retinal layers to separate them for subsequent investigations. Another important challenge commonly faced is the lack of clarity of the layer boundaries in retina OCT scans, which compels the research of super-resolving the images for improved clarity.
Deep learning pipelines have stimulated substantial progress for the segmentation tasks. Generative adversarial networks (GANs) are a prominent field of deep learning which achieved astonishing performance in semantic segmentation. Conditional adversarial networks as a general-purpose solution to image-to-image translation problems not only learn the mapping from the input image to the output image but also learn a loss function to train this mapping. We propose a GAN-based segmentation model and evaluate incorporating popular networks, namely, U-Net and ResNet, in the GAN architecture with additional blocks of transposed convolution and sub-pixel convolution for the task of upscaling OCT images from low to high resolution by a factor of four. We also incorporate the Dice loss as an additional reconstruction loss term to improve the performance of this joint optimization task. Our best model configuration empirically achieved the Dice coefficient of 0.867 and mIOU of 0.765
3D Medical Image Segmentation based on multi-scale MPU-Net
The high cure rate of cancer is inextricably linked to physicians' accuracy
in diagnosis and treatment, therefore a model that can accomplish
high-precision tumor segmentation has become a necessity in many applications
of the medical industry. It can effectively lower the rate of misdiagnosis
while considerably lessening the burden on clinicians. However, fully automated
target organ segmentation is problematic due to the irregular stereo structure
of 3D volume organs. As a basic model for this class of real applications,
U-Net excels. It can learn certain global and local features, but still lacks
the capacity to grasp spatial long-range relationships and contextual
information at multiple scales. This paper proposes a tumor segmentation model
MPU-Net for patient volume CT images, which is inspired by Transformer with a
global attention mechanism. By combining image serialization with the Position
Attention Module, the model attempts to comprehend deeper contextual
dependencies and accomplish precise positioning. Each layer of the decoder is
also equipped with a multi-scale module and a cross-attention mechanism. The
capability of feature extraction and integration at different levels has been
enhanced, and the hybrid loss function developed in this study can better
exploit high-resolution characteristic information. Moreover, the suggested
architecture is tested and evaluated on the Liver Tumor Segmentation Challenge
2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net
shows excellent segmentation results. The dice, accuracy, precision,
specificity, IOU, and MCC metrics for the best model segmentation results are
92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding
indicators in various aspects illustrate the exceptional performance of this
framework in automatic medical image segmentation.Comment: 37 page
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
Meningiomas are the most common type of primary brain tumor, accounting for
approximately 30% of all brain tumors. A substantial number of these tumors are
never surgically removed but rather monitored over time. Automatic and precise
meningioma segmentation is therefore beneficial to enable reliable growth
estimation and patient-specific treatment planning. In this study, we propose
the inclusion of attention mechanisms over a U-Net architecture: (i)
Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a
3D MRI volume as input. Attention has the potential to leverage the global
context and identify features' relationships across the entire volume. To limit
spatial resolution degradation and loss of detail inherent to encoder-decoder
architectures, we studied the impact of multi-scale input and deep supervision
components. The proposed architectures are trainable end-to-end and each
concept can be seamlessly disabled for ablation studies. The validation studies
were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes
from St. Olavs University Hospital, Trondheim, Norway. For the best performing
architecture, an average Dice score of 81.6% was reached for an F1-score of
95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml
were occasionally missed hence reaching an overall recall of 93%. Leveraging
global context from a 3D MRI volume provided the best performances, even if the
native volume resolution could not be processed directly. Overall, near-perfect
detection was achieved for meningiomas larger than 3ml which is relevant for
clinical use. In the future, the use of multi-scale designs and refinement
networks should be further investigated to improve the performance. A larger
number of cases with meningiomas below 3ml might also be needed to improve the
performance for the smallest tumors.Comment: 16 pages, 5 figures, 3 tables. Submitted to Artificial Intelligence
in Medicin
Pine wilt disease spreading prevention system using semantic segmentation
Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There is no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learning-based semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease
Workflow for reducing semantic segmentation annotation time
Abstract. Semantic segmentation is a challenging task within the field of pattern recognition from digital images. Current semantic segmentation methods that are based on neural networks show great promise in accurate pixel-level classification, but the methods seem to be limited at least to some extent by the availability of accurate training data. Semantic segmentation training data is typically curated by humans, but the task is rather slow and tedious even for humans. While humans are fast at checking whether a segmentation is accurate or not, creating segmentations is rather slow as the human visual system becomes limited by physical interfaces such as hand coordination for drawing segmentations by hand. This thesis evaluates a workflow that aims to reduce the need for drawing segmentations by hand to create an accurate set of training data.
A publicly available dataset is used as the starting-point for the annotation process, and four different evaluation sets are used to evaluate the introduced annotation workflow in labour efficiency and annotation accuracy.
Evaluation of the results indicates that the workflow can produce annotations that are comparable to manually corrected annotations in accuracy while requiring significantly less manual labour to produce annotations.Työnkulku semanttisen segmentoinnin annotointiajan vähentämiseen. Tiivistelmä. Semanttinen segmentointi on haastava osa-alue hahmontunnistusta digitaalisista kuvista. Tämänhetkiset semanttiset segmentaatiomenetelmät, jotka perustuvat neuroverkkoihin, osoittavat suurta potentiaalia tarkassa pikselitason luokittelussa, mutta ovat ainakin osittain tarkan koulutusdatan saatavuuden rajoittamia. Semanttisen segmentaation koulutusdata on tyypillisesti täysin ihmisten annotoimaa, mutta segmentaatioiden annotointi on hidasta ja pitkäveteistä. Vaikka ihmiset ovat nopeita tarkistamaan ovatko annotaatiot tarkkoja, niiden luonti on hidasta, koska ihmisen visuaalisen järjestelmän nopeuden ja tarkkuuden rajoittavaksi tekijäksi lisätään fyysinen rajapinta, kuten silmä-käsi-koordinaatio piirtäessä segmentaatioita käsin. Tämä opinnäytetyö arvioi kokonaisvaltaisen semanttisten segmentaatioiden annotointitavan, joka pyrkii vähentämään käsin piirtämisen tarvetta tarkan koulutusdatan luomiseksi.
Julkisesti saatavilla olevaa datajoukkoa käytetään annotoinnin lähtökohtana, ja neljää erilaista evaluointijoukkoa käytetään esitetyn annotointitavan työtehokkuuden sekä annotaatiotarkkuuden arviointiin.
Evaluaatiotulokset osoittavat, että esitetty tapa kykenee tuottamaan annotaatioita jotka ovat yhtä tarkkoja kuin käsin korjatut annotaatiot samalla merkittävästi vähentäen käsin tehtävän työn määrää
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