TY - GEN
T1 - Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions
AU - Dmitriev, Konstantin
AU - Kaufman, Arie E.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Diagnosis of various pancreatic lesions in CT images is a challenging task owing to a significant overlap in their imaging appearance. An accurate diagnosis of pancreatic lesions and the assessment of their malignant progression, or the grade of dysplasia, is crucial for optimal patient management. Typically, the grade of dysplasia is confirmed histologically via biopsy, yet certain radiological findings, including extrapancreatic, can serve as diagnostic clues of the disease progression. This work introduces a novel method of transforming intermediate activations for processing intact imaging data of varying sizes with convnets with linear layers. Our method allows to efficiently leverage the 3D information of the entire abdominal CT scan to acquire a holistic picture of all radiological findings for an improved and more precise classification of pancreatic lesions. Our model outperforms current state-of-the-art methods in classifying four most common lesion types (by 2.92%), while additionally diagnosing the grade of dysplasia. We conduct a set of experiments to illustrate the effects of a holistic CT analysis and the auxiliary diagnostic data on the accuracy of the final diagnosis.
AB - Diagnosis of various pancreatic lesions in CT images is a challenging task owing to a significant overlap in their imaging appearance. An accurate diagnosis of pancreatic lesions and the assessment of their malignant progression, or the grade of dysplasia, is crucial for optimal patient management. Typically, the grade of dysplasia is confirmed histologically via biopsy, yet certain radiological findings, including extrapancreatic, can serve as diagnostic clues of the disease progression. This work introduces a novel method of transforming intermediate activations for processing intact imaging data of varying sizes with convnets with linear layers. Our method allows to efficiently leverage the 3D information of the entire abdominal CT scan to acquire a holistic picture of all radiological findings for an improved and more precise classification of pancreatic lesions. Our model outperforms current state-of-the-art methods in classifying four most common lesion types (by 2.92%), while additionally diagnosing the grade of dysplasia. We conduct a set of experiments to illustrate the effects of a holistic CT analysis and the auxiliary diagnostic data on the accuracy of the final diagnosis.
UR - https://www.scopus.com/pages/publications/85092692384
U2 - 10.1007/978-3-030-59713-9_28
DO - 10.1007/978-3-030-59713-9_28
M3 - Conference contribution
AN - SCOPUS:85092692384
SN - 9783030597122
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 283
EP - 293
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -