Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction fusion. They often overlook the different contributions visual representation intelligent decisions among images. Motivated by this discovery, paper proposes an asymmetric adaptive heterogeneous network with modality For extraction, it uses a two-stream feature-bridging extract complementary features...

The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress due to powerful local modeling ability. However, tumors are frequently pattern-agnostic, i.e. variable shape, size and location, which can not be effectively matched by traditional CNN-based with regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S <sup...

Objective.In breast diagnostic imaging, the morphological variability of tumors and inherent ambiguity ultrasound images pose significant challenges. Moreover, multi-task computer-aided diagnosis systems in imaging may overlook relationships between pixel-wise segmentation categorical classification tasks.Approach.In this paper, we propose a learning network with deep inter-task interactions that exploits inherently relations two tasks. First, fuse self-task attention cross-task mechanisms...

Recently, extraction of blood vessels has aroused widespread interests in medical image analysis. In this work, to accelerate convergence speed and enhance the representation for discriminative features, we introduce residual block structure ResNet into 3D U-Net, construct a new Residual U-Net architect segment hepatic portal veins from abdominal CT volumes. addition, develop weighted Dice loss function cope with challenges pixel imbalance, vessel boundary segmentation small segmentation....

This paper presents a factorization based active contour model for 2-phase texture segmentation. We utilize the local spectral histogram as features, and then establish novel energy function on theory of matrix decomposition. Unlike existing methods, we only choose combination weights from object region background to handle motion curve. compare proposed method recently methods experiments are performed synthetic real-world images. The experimental results show that our is more robust...

Abstract Objective. Over the past years, convolutional neural networks based methods have dominated field of medical image segmentation. But main drawback these is that they difficulty representing long-range dependencies. Recently, Transformer has demonstrated super performance in computer vision and also been successfully applied to segmentation because self-attention mechanism dependencies encoding on images. To best our knowledge, only a few works focus cross-modalities using...

Automatic liver tumour segmentation is an important step towards digital medical research, clinical diagnosis and therapy planning. However, the existence of noise, low contrast heterogeneity make automatic remaining open challenge. In this work, we focus on a novel method to segment in abdomen images from CT scans using fully convolutional networks (FCN) non-negative matrix factorization (NMF) based deformable model. We train FCN for semantic preprocessed training data by BM3D. The...

Medical image segmentation plays an important role in digital medical research, and therapy planning delivery. However, the presence of noise low contrast renders automatic liver extremely challenging task. In this study, we focus on a variational approach to computed tomography scan volumes semiautomatic slice-by-slice manner. method, one slice is selected its connected component region determined manually initialize subsequent process. From guiding slice, execute proposed method downward...

Abstract Objective. Recently, deep learning techniques have found extensive application in accurate and automated segmentation of tumor regions. However, owing to the variety shapes, complex types, unpredictability spatial distribution, still faces major challenges. Taking cues from supervision adversarial learning, we devised a cascade-based methodology incorporating multi-scale difficult-region this study tackle these Approach. Overall, method adheres coarse-to-fine strategy, first roughly...

Images with weak contrast, overlapped noise and texture of the object background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its formulation consists edge-based term, region-based term shape constraint term. The is constructed using newly defined edge stopping function. derived from parameter-free Gaussian probability density function (pdf) multiple kernel are used to...

This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion isolated region detection operations eliminate image patches unrelated with object regions. And then fused features combined LSVD Gabor can be constructed structure in many scene. During segmentation stage,...

Medical image segmentation plays an important role in digital medical research, therapy planning, and computer aided diagnosis. However, the existence of noise low contrast make automatic liver remains open challenge. In this work we focus on a novel variational semi-automatic method. First, used signed distance functions (SDF) representing pattern shapes to build statistical shape model. Then global Gaussian fitting energy enforced local feature were established guide PCA-based topological...

Head and neck (H&N) cancers are among the most common worldwide (5th leading cancer by incidence). Accurate segmentation of H&N tumors can improve early diagnosis rate for timely treatment. tumor challenge is equidensity between surrounding tissues, which shows low contrast in CT. In contrast, PET images reflect distinction lesion region normal tissue through metabolic activity but show spatial resolution. With underlying assumption that each modality contains complementary information, we...

Multi-modal medical image segmentation plays a vital role in clinical applications such as auxiliary diagnosis and surgical planning. However, it is still challenging task to extract fuse complementary information crossing modalities. Focusing on this target, paper proposes dual-attention deep fusion network for multi-modal segmentation. Generally, proposed follows the typical encoder-decoder skip connection workflow. Unlike existing methods, applies two attention modules deeply features...