Catalytically active and corrosion resistant cobalt-based thin films

Linder, Clara

doi:10.3384/9789179292171

Cited by 1 publication

(1 citation statement)

References 66 publications

(92 reference statements)

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“…[57][58][59][60] Recently, DL methods have evolved for this purpose (often under the name "attention mechanisms") with improved results. [61][62][63][64][65][66][67][68][69][70][71][72] These methods do not qualify as representatives of the R-step in the spirit of the RD-paradigm of the HI-strategy mentioned earlier for several reasons. (i) They require large swaths of annotated data sets, making them less efficient for training/model building (annotation time and computational time) compared to model-based methods such as Refs.…”

Section: Related Literaturementioning

confidence: 99%

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring

et al. 2022

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Background Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end deep learning (DL) networks, are weak in garnering high‐level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge. Purpose We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation. Methods The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI‐based automatic anatomy recognition object recognition (AAR‐R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL‐based recognition (DL‐R), which refines the coarse recognition results of AAR‐R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR‐R fuzzy model of each object guided by the BBs output by DL‐R; and (v) DL‐based delineation (DL‐D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system. Results The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground‐truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53 mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto‐contours and clinically drawn contours. Conclusions The HI system is observed to behave like an expert human in robustness in the contouring task but vast...

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