Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods. 

TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images

Automatic segmentation of medical images can provide a reliable basis for clinical analysis and disease diagnosis. Due to the varying sizes and appearances of different pathological lesions, most convolutional neural networks (CNNs) approaches are unable to fully extract multi-scale features and usually lead to false positive and false negative segmentation. Multi-scale medical image segmentation still faces significant challenges. By designing different levels of skip connection modules (SCMs), local multi-scale context is proposed. We also adopted feature pyramid network (FPN) to connect SCMs to extract global multi-scale features. Local multi-scale context and global multi-scale feature constitute a multi-level multi-scale feature extraction network (MMS-Net). Multi-level multi-scale feature was extracted and accurate spatial information was also retained. Comprehensive experiments (training vs. testing: 8:2) on five public datasets show that the proposed MMS-Net achieved accurate segmentation and outperformed the state-of-the-art algorithms. Besides, when only using 50% and 20% of the training dataset, MMS-Net still obtained competitive segmentation results. 

MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation

Automatic classification and segmentation of medical images play essential roles in computer-aided diagnosis. Deep convolutional neural networks (DCNNs) have shown their advantages on image classification and segmentation. However, they have not achieved the same success on medical images as they have done on natural images. In this paper, two challenges are exploited for DCNNs on medical images, including 1) lack of feature diversity; 2) neglect of small lesions. These two issues heavily influence the classification and segmentation performances. To improve the performance of DCNN on medical images, similarity-aware attention (simi-attention) module is proposed, including a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-similarity-aware Channel Attention (FCA) and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Region-similarity-aware Spatial Attention (RSA). Our simi-attention provides three advantages: 1) higher accuracy can be achieved since it extracts both diverse and discriminant features from medical images via our FCA and RSA; 2) the lesions can be exactly focused and located by it even for the data with low intensity contrast and small lesions; 3) it does not increase the complexity of backbone models due to NO trainable parameters in its module. The experimental results are conducted on both classification and segmentation tasks under four public medical classification datasets and two public medical segmentation datasets. The visualization results show that our simi-attention can accurately focuses on the lesions for classification and generate fine segmentation results even for small objects. The overall performances show that our simi-attention can significantly improve the performances of backbone models and outperforms compared attention models on most of datasets for both classification and segmentation.

Parameter-Free Similarity-Aware Attention Module for Medical Image Classification and Segmentation

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses. 

Beyond Radiologist-Level Liver Lesion Detection on Multi-phase Contrast-enhanced CT Images by Deep Learning

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts. The proposed SDR-Former utilizes a streamlined Siamese Neural Network (SNN) to process multi-phase imaging inputs, possessing robust feature representations while maintaining computational efficiency. The weight-sharing feature of the SNN is further enriched by a hybrid Dual-Resolution Transformer (DR-Former), comprising a 3D Convolutional Neural Network (CNN) and a tailored 3D Transformer for processing high- and low-resolution images, respectively. This hybrid sub-architecture excels in capturing detailed local features and understanding global contextual information, thereby, boosting the SNN’s feature extraction capabilities. Additionally, a novel Adaptive Phase Selection Module (APSM) is introduced, promoting phase-specific intercommunication and dynamically adjusting each phase’s influence on the diagnostic outcome. The proposed SDR-Former framework has been validated through comprehensive experiments on two clinically collected datasets: a 3-phase CT dataset and an 8-phase MR dataset. The experimental results affirm the efficacy of the proposed framework. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public. This pioneering dataset, being the first publicly available multi-phase MR dataset in this field, also underpins the MICCAI LLD-MMRI Challenge. The dataset is publicly available at: https://github.com/LMMMEng/LLD-MMRI-Dataset.

SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging

M3Net: A multi-scale multi-view framework for multi-phase pancreas segmentation based on cross-phase non-local attention.

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M3Net: A multi-scale multi-view framework for multi-phase pancreas segmentation based on cross-phase non-local attention.