![cross entropy cross entropy](https://miro.medium.com/max/2428/1*LOn-ZNM_kPpjfOSI6X8vrg.png)
Automated detection and segmentation would immediately impact the clinical workflow in radiotherapy, one of the most common treatment modalities for lung cancer 2. However, much work is still to be done in the field of lung cancer, especially in screening and early detection. Recent advances in treatment (immune checkpoint inhibitors, tyrosine kinase inhibitors) has significantly improved survival times for subgroups of patients. Lung cancer is the deadliest of all cancers afflicting both sexes, accounting for 18.4% of the total cancer deaths worldwide in 2018, almost equal to breast and colon cancers combined 1.
Cross entropy manual#
Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.
![cross entropy cross entropy](https://assignu.com/wp-content/uploads/2021/05/Pasted-145.jpg)
Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes.
![cross entropy cross entropy](https://miro.medium.com/max/3028/1*UwPD3hNltoBbxYxS517MDA.png)
Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Nature Communications volume 13, Article number: 3423 ( 2022)ĭetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. Automated detection and segmentation of non-small cell lung cancer computed tomography images