Local integration of deep learning for advanced visualization in congenital heart disease surgical planning

Nainamalai, Varatharajan; Lippert, Matthias; Brun, Henrik; Elle, Ole Jakob; Kumar, Rahul Prasanna

doi:10.1016/j.ibmed.2022.100055

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Over the recent years, several platforms facilitating the development of deep learning neural networks for medical imaging have emerged from the artificial intelligence community [ 10 , 38 ]. One of these is NiftyNet [ 39 ]-an open-source neural networks platform based on the Tensorflow framework.…”

Section: Methodsmentioning

confidence: 99%

“…The manual ground truth annotation was performed using selected images from each of the four MR sequences depending on which tissue to annotate, taking advantage of the inherent Fig. 2 The neural network architecture medical imaging have emerged from the artificial intelligence community [10,38]. One of these is NiftyNet [39]-an open-source neural networks platform based on the Tensorflow framework.…”

Section: Sample Selection and Datasetmentioning

confidence: 99%

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Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Kulseng¹,

Nainamalai

Grøvik

et al. 2023

BMC Musculoskelet Disord

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Background To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. Methods The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks. Results Combining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics. Conclusions The convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation.

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Section: Methodsmentioning

confidence: 99%

Section: Sample Selection and Datasetmentioning

confidence: 99%

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Kulseng¹,

Nainamalai

Grøvik

et al. 2023

BMC Musculoskelet Disord

Self Cite

View full text Add to dashboard Cite

show abstract

“…In general, the evaluation of segmentation results can be classified using a confusion matrix with true positive (TP), false positive (FP), false negative (FN), and true negative (TN). We used the following performance metric for the evaluation: The Dice similarity coefficient (DSC or Dice score) is a measure of the spatial overlap between the predicted segmentation and the manual segmentation written as

The Jaccard index (JAC) is defined as the intersection over the union of the predicted segmentation and the manual segmentation defined by

Over segmentation (OS) measures the overlapping of the prediction and the complement of label voxels in the ground truth over a union of ground truth and prediction defined by

where GT is the ground truth, Pred represents the prediction, and

complement of a class in the manual segmentation [43] , [44] . Under segmentation (US) measures overlapping of the complement of the prediction (

) and the ground truth over the union of ground truth and prediction defined by

…”

Section: Methodsmentioning

confidence: 99%

Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images

Nainamalai

Prasad

Pelanis

et al. 2022

European Journal of Radiology Open

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Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students

Brun,

Lippert,

Langø

et al. 2024

Anatomical Sciences Ed

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Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students′ understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.

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Local integration of deep learning for advanced visualization in congenital heart disease surgical planning

Cited by 9 publications

References 14 publications

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images

Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students

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