Surgery for brain cancer is a major problem in neurosurgery. The diffuse
infiltration into the surrounding normal brain by these tumors makes their
accurate identification by the naked eye difficult. Since surgery is the common
treatment for brain cancer, an accurate radical resection of the tumor leads to
improved survival rates for patients. However, the identification of the tumor
boundaries during surgery is challenging. Hyperspectral imaging is a
noncontact, non-ionizing and non-invasive technique suitable for medical
diagnosis. This study presents the development of a novel classification method
taking into account the spatial and spectral characteristics of the
hyperspectral images to help neurosurgeons to accurately determine the tumor
boundaries in surgical-time during the resection, avoiding excessive excision
of normal tissue or unintentionally leaving residual tumor. The algorithm
proposed in this study to approach an efficient solution consists of a hybrid
framework that combines both supervised and unsupervised machine learning
methods. To evaluate the proposed approach, five hyperspectral images of
surface of the brain affected by glioblastoma tumor in vivo from five different
patients have been used. The final classification maps obtained have been
analyzed and validated by specialists. These preliminary results are promising,
obtaining an accurate delineation of the tumor area.

开发了一种考虑到高光谱图像的空间和光谱特征的新型分类方法，帮助神经外科医生在手术过程中准确确定肿瘤边界，避免过度切除正常组织或意外剩余肿瘤，初步结果有望获得肿瘤区域的准确划分。

手术过程中基于高光谱图像的脑癌检测的时空谱分类

Spatio-spectral classification of hyperspectral images for brain cancer  detection during surgical operations

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。