Accurate segmentation of biomedical images is of utmost importance for the classification in various physiopathological conditions. The conventional method of segmentation of biomedical images is manual labeling by image processing and biomedical experts which is a hectic as well as a tedious task to carry. Also, it takes a longer time to label, and still, inter-operative error possibilities exist. To overcome these problems, an end-to-end segmentation pipeline is required with minimal or no operator intervention. Here, we propose to use a cascade of conventional and deep learning methods to obtain accurate segmentation. First, seed region growing, random walker, and K-means clustering are used individually to roughly delineate the region of interest, and then, the generated masks are used to train the deep learning model to get the final segmentation. The proposed method is validated for the segmentation of the left ventricle in cardiac magnetic resonance images (ACDC Dataset). The method is again cross-validated with another dataset on which the model is not previously trained. Further, the segmentation pipeline is experimented to work with a lesser number of images in three different levels (Degree I, II, and III). Dice score, Jaccard index, and Hausdorff distance are used as metrics to show the effectiveness of the proposed weakly supervised method. The results obtained by different methods are competitive enough to the state-of-the-art(supervised) method. The proposed cascaded weakly supervised method paves the way towards the unsupervised segmentation of biomedical images with minimal or no manual intervention.

生物医学图像的准确分割对于各种生理病理条件下的分类至关重要。传统的生物医学图像分割方法是由图像处理和生物医学专家手动标记，这是一项繁忙且繁琐的工作。此外，标记需要更长的时间，并且仍然存在操作间错误的可能性。为了克服这些问题，端到端的分割流​​水线需要最少或没有操作员干预。在这里，我们建议使用级联的传统和深度学习方法来获得准确的分割。一、seed region growing、random walker、K-均值聚类分别用于粗略地勾画感兴趣区域，然后，生成的掩码用于训练深度学习模型以获得最终的分割。所提出的方法在心脏磁共振图像（ACDC 数据集）中对左心室的分割进行了验证。该方法再次与先前未训练模型的另一个数据集进行交叉验证。此外，还对分割管道进行了实验，以在三个不同级别（I、II 和 III 级）处理较少数量的图像。Dice 分数、Jaccard 指数和 Hausdorff 距离用作指标来显示所提出的弱监督方法的有效性。通过不同方法获得的结果与最先进的（监督）方法相比具有足够的竞争力。