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 clinical datasets: a three-phase CT dataset and an
eight-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
accessible at:this https URL

提出了一种新的 Siamese Dual-Resolution Transformer (SDR-Former) 框架，特别针对不同相数的 3D 多相 CT 和 MR 成像中的肝脏病变分类，利用流体动力学 Siamese Neural Network (SNN) 处理多相成像输入，通过混合的 Dual-Resolution Transformer (DR-Former) 子架构以及新颖的 Adaptive Phase Selection Module (APSM) 实现特征提取和相特定的相互通信，从而提高了病变分类的准确性。

SDR-Former：一种用于肝脏病变分类的孪生双分辨率 Transformer，使用 3D 多相成像

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

Liver cancer has high morbidity and mortality rates in the world. Multi-phase
CT is a main medical imaging modality for detecting/identifying and diagnosing
liver tumors. Automatically detecting and classifying liver lesions in CT
images have the potential to improve the clinical workflow. This task remains
challenging due to liver lesions' large variations in size, appearance, image
contrast, and the complexities of tumor types or subtypes. In this work, we
customize a multi-object labeling tool for multi-phase CT images, which is used
to curate a large-scale dataset containing 1,631 patients with four-phase CT
images, multi-organ masks, and multi-lesion (six major types of liver lesions
confirmed by pathology) masks. We develop a two-stage liver lesion detection
pipeline, where the high-sensitivity detecting algorithms in the first stage
discover as many lesion proposals as possible, and the lesion-reclassification
algorithms in the second stage remove as many false alarms as possible. The
multi-sensitivity lesion detection algorithm maximizes the information
utilization of the individual probability maps of segmentation, and the
lesion-shuffle augmentation effectively explores the texture contrast between
lesions and the liver. Independently tested on 331 patient cases, the proposed
model achieves high sensitivity and specificity for malignancy classification
in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and
in the noncontrast CT (97.3%, 95.7%, screening setting).

开发了一个二阶段的肝癌检测管道，其中高灵敏度探测算法在第一阶段发现尽可能多的病变提议，而第二阶段的病变重新分类算法则尽可能删除尽可能多的虚警。

肝部 CT 扫描中超高性能病变检测和虚假阳性去除的级联方法

A Cascaded Approach for ultraly High Performance Lesion Detection and  False Positive Removal in Liver CT Scans

Automatic segmentation of liver lesions is a fundamental requirement towards
the creation of computer aided diagnosis (CAD) and decision support systems
(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
features and a priori knowledge of the user. As such, these methods are
difficult to adopt within a clinical environment. Recently, deep learning
methods based on fully convolutional networks (FCNs) have been successful in
many segmentation problems primarily because they leverage a large labelled
dataset to hierarchically learn the features that best correspond to the
shallow visual appearance as well as the deep semantics of the areas to be
segmented. However, FCNs based on a 16 layer VGGNet architecture have limited
capacity to add additional layers. Therefore, it is challenging to learn more
discriminative features among different classes for FCNs. In this study, we
overcome these limitations using deep residual networks (ResNet) to segment
liver lesions. ResNet contain skip connections between convolutional layers,
which solved the problem of the training degradation of training accuracy in
very deep networks and thereby enables the use of additional layers for
learning more discriminative features. In addition, we achieve more precise
boundary definitions through a novel cascaded ResNet architecture with
multi-scale fusion to gradually learn and infer the boundaries of both the
liver and the liver lesions. Our proposed method achieved 4th place in the ISBI
2017 Liver Tumor Segmentation Challenge by the submission deadline.

本研究采用一种基于深度残余网络 (ResNet) 的肝脏病变分割方法，通过级联 ResNet 架构和多尺度融合生成更精确的边界定义，该方法在 ISBI 2017 肝脏瘤分割挑战中获得第四名。