5 research outputs found
Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
Robust and accurate segmentation for elongated physiological structures is
challenging, especially in the ambiguous region, such as the corneal
endothelium microscope image with uneven illumination or the fundus image with
disease interference. In this paper, we present a spatial and scale
uncertainty-aware network (SSU-Net) that fully uses both spatial and scale
uncertainty to highlight ambiguous regions and integrate hierarchical structure
contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps
using Monte Carlo dropout to approximate Bayesian networks. Based on these
spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA)
module to guide the model to focus on ambiguous regions. Second, we extract the
uncertainty under different scales and propose the multi-scale
uncertainty-aware (MSUA) fusion module to integrate structure contexts from
hierarchical predictions, strengthening the final prediction. Finally, we
visualize the uncertainty map of final prediction, providing interpretability
for segmentation results. Experiment results show that the SSU-Net performs
best on cornea endothelial cell and retinal vessel segmentation tasks.
Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more
accurate and robust
PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification
Spatial attention mechanism has been widely incorporated into deep
convolutional neural networks (CNNs) via long-range dependency capturing,
significantly lifting the performance in computer vision, but it may perform
poorly in medical imaging. Unfortunately, existing efforts are often unaware
that long-range dependency capturing has limitations in highlighting subtle
lesion regions, neglecting to exploit the potential of multi-scale pixel
context information to improve the representational capability of CNNs. In this
paper, we propose a practical yet lightweight architectural unit, Pyramid Pixel
Context Recalibration (PPCR) module, which exploits multi-scale pixel context
information to recalibrate pixel position in a pixel-independent manner
adaptively. PPCR first designs a cross-channel pyramid pooling to aggregate
multi-scale pixel context information, then eliminates the inconsistency among
them by the well-designed pixel normalization, and finally estimates per pixel
attention weight via a pixel context integration. PPCR can be flexibly plugged
into modern CNNs with negligible overhead. Extensive experiments on five
medical image datasets and CIFAR benchmarks empirically demonstrate the
superiority and generalization of PPCR over state-of-the-art attention methods.
The in-depth analyses explain the inherent behavior of PPCR in the
decision-making process, improving the interpretability of CNNs.Comment: 10 page
Angle-closure assessment in anterior segment OCT images via deep learning
Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients’ eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy
Nuclear cataract classification in anterior segment OCT based on clinical global–local features
Abstract Nuclear cataract (NC) is a priority ocular disease of blindness and vision impairment globally. Early intervention and cataract surgery can improve the vision and life quality of NC patients. Anterior segment coherence tomography (AS-OCT) imaging is a non-invasive way to capture the NC opacity objectively and quantitatively. Recent clinical research has shown that there exists a strong opacity correlation relationship between NC severity levels and the mean density on AS-OCT images. In this paper, we present an effective NC classification framework on AS-OCT images, based on feature extraction and feature importance analysis. Motivated by previous clinical knowledge, our method extracts the clinical global–local features, and then applies Pearson’s correlation coefficient and recursive feature elimination methods to analyze the feature importance. Finally, an ensemble logistic regression is employed to distinguish NC, which considers different optimization methods’ characteristics. A dataset with 11,442 AS-OCT images is collected to evaluate the method. The results show that the proposed method achieves 86.96% accuracy and 88.70% macro-sensitivity, respectively. The performance comparison analysis also demonstrates that the global–local feature extraction method improves about 2% accuracy than the single region-based feature extraction method