ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

Abstract

Purpose: The automated segmentation of each lung and trachea in CT scans is commonly taken as a solved problem. Indeed, existing approaches may easily fail in the presence of some abnormalities caused by a disease, trauma, or previous surgery. For robustness, we present ALTIS (implementation is available at http://lids.ic.unicamp.br/downloads)a fast automatic lung and trachea CT-image segmentation method that relies on image features and relative shape-and intensity-based characteristics less affected by most appearance variations of abnormal lungs and trachea. Methods: ALTIS consists of a sequence of image foresting transforms (IFTs) organized in three main steps: (a) lung-and-trachea extraction, (b) seed estimation inside background, trachea, left lung, and right lung, and (c) their delineation such that each object is defined by an optimum-path forest rooted at its internal seeds. We compare ALTIS with two methods based on shape models (SOSM-S and MALF), and one algorithm based on seeded region growing (PTK). Results:The experiments involve the highest number of scans found in literature -1255 scans, from multiple public data sets containing many anomalous cases, being only 50 normal scans used for training and 1205 scans used for testing the methods. Quantitative experiments are based on two metrics, DICE and ASSD. Furthermore, we also demonstrate the robustness of ALTIS in seed estimation. Considering the test set, the proposed method achieves an average DICE of 0.987 for both lungs and 0.898 for the trachea, whereas an average ASSD of 0.938 for the right lung, 0.856 for the left lung, and 1.316 for the trachea. These results indicate that ALTIS is statistically more accurate and considerably faster than the compared methods, being able to complete segmentation in a few seconds on modern PCs. Conclusion: ALTIS is the most effective and efficient choice among the compared methods to segment left lung, right lung, and trachea in anomalous CT scans for subsequent detection, segmentation, and quantitative analysis of abnormal structures in the lung parenchyma and pleural space.

Automatic clustering method to segment COVID-19 CT images

“…Three measures are used to assess the performance of all algorithms to evaluate the quality of the segmentation process. These measures are peak signal-to-noise ratio (PSNR) [39] as in Eq (13), the structural similarity index (SSIM) as in Eq 14…”

“…More so, they showed that this method could successfully segment CT images with complex boundaries. Sousa et al [13] proposed an automatic CT images segmentation method for lung and trachea. Their proposed method, called ALTIS showed good performance in detecting abnormal structures in CT images.…”

“…The proposed level-set formulation combines active dense displacement estimation with Chan-Vese segmentation. Sousa et al [13] presented a segmentation method called ALTIS to segment lung and trachea in CT images.…”

Superpixel Generation by the Iterative Spanning Forest Using Object Information

“…Such solution is efficiently obtained through an adaptation of the Dijkstra's algorithm for more general pathcost functions -also known as connectivity functions. Due to its performance, the IFT has been widely used in different contexts and purposes [28]- [30].…”

Unsupervised brain anomaly detection in MR images

“…The image operation resumes to a post-processing of the forest attributes, such as the root labels, optimum paths, and connectivity values. IFT has been successfully applied in dierent domains, such as image ltering [116], image descriptor [117,118], segmentation [41,[119][120][121][122][123], superpixel segmentation [40,61,124], representation [125], (semi) supervised classication [126][127][128], and data clustering [129,130].…”