Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia.
Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 120611, Ethiopia.
Sensors (Basel). 2023 Jul 29;23(15):6781. doi: 10.3390/s23156781.
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
肺结核（PTB）是一种影响肺部的细菌感染。PTB 仍然是全球死亡率最高的传染病之一。胸部 X 线摄影是一种常用于诊断 PTB 的技术。放射科医生通过检查患者胸部 X 射线（CXR）中的放射影像特征来确定 PTB 的严重程度和阶段。CXR 上最常见的放射影像特征包括空洞、实变、肿块、胸腔积液、钙化和结节。识别这些 CXR 特征将有助于医生诊断患者。然而，识别这些复杂疾病的放射影像特征具有挑战性，准确性取决于放射科医生的经验和专业水平。因此，研究人员提出了深度学习（DL）技术来检测和标记 CXR 中的肺结核感染区域。由于其从医学图像中检测疾病和分割表现区域的固有能力，文献中提出了 DL 模型。然而，完全监督的语义分割需要对大量像素进行逐点标记图像。由训练有素的医生对如此大量的数据进行注释存在一些挑战。首先，注释需要大量时间。其次，雇佣训练有素的医生的成本很高。此外，医学数据的主观性给标准化注释带来了困难。因此，人们越来越关注弱定位技术。因此，在本综述中，我们确定了从胸部 X 射线中用于弱监督的放射影像表现的分割和定位的方法。首先，我们确定了用于结核病识别的最常用的公共胸部 X 射线数据集。之后，我们讨论了在胸部 X 射线中弱定位结核病放射影像表现的方法。PTB 的弱监督定位可以突出对 DL 模型分类输出贡献最大的胸部 X 射线图像区域，并有助于精确定位患病区域。最后，我们讨论了在胸部 X 射线图像中弱监督技术定位 TB 表现区域的局限性和挑战。
