The accurate segmentation of breast tumors is an important prerequisite for
lesion detection, which has significant clinical value for breast tumor
research. The mainstream deep learning-based methods have achieved a
breakthrough. However, these high-performance segmentation methods are
formidable to implement in clinical scenarios since they always embrace high
computation complexity, massive parameters, slow inference speed, and huge
memory consumption. To tackle this problem, we propose LightBTSeg, a dual-path
joint knowledge distillation framework, for lightweight breast tumor
segmentation. Concretely, we design a double-teacher model to represent the
fine-grained feature of breast ultrasound according to different semantic
feature realignments of benign and malignant breast tumors. Specifically, we
leverage the bottleneck architecture to reconstruct the original Attention
U-Net. It is regarded as a lightweight student model named Simplified U-Net.
Then, the prior knowledge of benign and malignant categories is utilized to
design the teacher network combined dual-path joint knowledge distillation,
which distills the knowledge from cumbersome benign and malignant teachers to a
lightweight student model. Extensive experiments conducted on breast ultrasound
images (Dataset BUSI) and Breast Ultrasound Dataset B (Dataset B) datasets
demonstrate that LightBTSeg outperforms various counterparts.

我们提出了一种轻量级乳腺肿瘤分割的双路径联合知识蒸馏框架 LightBTSeg，通过使用深度学习方法和知识蒸馏技术，在乳腺超声图像中实现高性能的肿瘤分割。

LightBTSeg: 一种利用超声图像进行乳腺肿瘤分割的轻量级模型

LightBTSeg: A lightweight breast tumor segmentation model using  ultrasound images via dual-path joint knowledge distillation

Automatic segmentation of breast tumors from the ultrasound images is
essential for the subsequent clinical diagnosis and treatment plan. Although
the existing deep learning-based methods have achieved significant progress in
automatic segmentation of breast tumor, their performance on tumors with
similar intensity to the normal tissues is still not pleasant, especially for
the tumor boundaries. To address this issue, we propose a PBNet composed by a
multilevel global perception module (MGPM) and a boundary guided module (BGM)
to segment breast tumors from ultrasound images. Specifically, in MGPM, the
long-range spatial dependence between the voxels in a single level feature maps
are modeled, and then the multilevel semantic information is fused to promote
the recognition ability of the model for non-enhanced tumors. In BGM, the tumor
boundaries are extracted from the high-level semantic maps using the dilation
and erosion effects of max pooling, such boundaries are then used to guide the
fusion of low and high-level features. Moreover, to improve the segmentation
performance for tumor boundaries, a multi-level boundary-enhanced segmentation
(BS) loss is proposed. The extensive comparison experiments on both publicly
available dataset and in-house dataset demonstrate that the proposed PBNet
outperforms the state-of-the-art methods in terms of both qualitative
visualization results and quantitative evaluation metrics, with the Dice score,
Jaccard coefficient, Specificity and HD95 improved by 0.70%, 1.1%, 0.1% and
2.5% respectively. In addition, the ablation experiments validate that the
proposed MGPM is indeed beneficial for distinguishing the non-enhanced tumors
and the BGM as well as the BS loss are also helpful for refining the
segmentation contours of the tumor.

提出了一种 PBNet 方法，由多级全局感知模块（MGPM）和边界引导模块（BGM）组成，用于从超声图像中分割乳腺肿瘤。在 MGPM 中，建模了单层特征图中体素之间的长程空间依赖关系，然后融合了多级语义信息以提高模型对非增强肿瘤的识别能力。在 BGM 中，使用最大池化的膨胀和侵蚀效果从高级语义图中提取肿瘤边界，并利用这些边界来引导低级和高级特征的融合。通过对公开可用数据集和院内数据集进行的广泛对比实验表明，所提出的 PBNet 在定性可视化结果和定量评估指标方面优于现有的方法，Dice 分数、Jaccard 系数、特异性和 HD95 分别提高了 0.70%、1.1%、0.1% 和 2.5%。此外，割舍实验证明了所提出的 MGPM 对于区分非增强肿瘤以及 BGM 和 BS 损失对于优化肿瘤的分割轮廓也是有帮助的。

多级感知边界引导网络用于超声图像中乳腺病变分割

Multilevel Perception Boundary-guided Network for Breast Lesion  Segmentation in Ultrasound Images

The proliferative activity of breast tumors, which is routinely estimated by
counting of mitotic figures in hematoxylin and eosin stained histology
sections, is considered to be one of the most important prognostic markers.
However, mitosis counting is laborious, subjective and may suffer from low
inter-observer agreement. With the wider acceptance of whole slide images in
pathology labs, automatic image analysis has been proposed as a potential
solution for these issues. In this paper, the results from the Assessment of
Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The
challenge was based on a data set consisting of 12 training and 11 testing
subjects, with more than one thousand annotated mitotic figures by multiple
observers. Short descriptions and results from the evaluation of eleven methods
are presented. The top performing method has an error rate that is comparable
to the inter-observer agreement among pathologists.

本研究介绍了自动图像分析在乳腺肿瘤组织分析中的应用，通过分析 AMIDA13 数据集和 11 种不同的算法，提出了一种性能可与病理学家间交互的标准相媲美的图像分析算法。