Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid
The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model's stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningMorphological AnalysisSimilar Papers 제목 키워드 기반
Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation
Encoder-decoder architectures are widely adopted for medical image segmentation tasks. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve m…
Boundary DetectionDecoderImage SegmentationMedical Image Segmentation+2Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning
In recent years, neural networks have proven to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks with conventional architectures cannot a…
Chinese Word SegmentationTransfer LearningDeconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and deconvolution operations. 'Unpooling' upsam…
Lesion SegmentationSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issu…
Semi-supervised Medical Image SegmentationBidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation
Semi-supervised medical image segmentation is an effective method for addressing scenarios with limited labeled data. Existing methods mainly rely on frameworks such as mean teacher and dual-stream consistency learning. …
Semi-supervised Medical Image Segmentation